{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming With Python\n",
    "##### For individuals who have used R or Stata before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Jupyter Notebook\n",
    "Jupyter is like R Studio: it is an interface that you can use to access python and manage your code/output/packages. You do not have to use Jupyter - you can code with python directly in the terminal.\n",
    "\n",
    "What we are working in is a notebook, kind of like a do-file. It's a file where you can document, code and have output in one place! You can also use python and jupyter to create versatile, all-in-one-place presentations (including running code while presenting, dynamic pdfs and more!\n",
    "\n",
    "Those who use R will be familiar with the way Python works - it uses objects that can be different types of things like dataframes, arrays, series, lists, or dictionaries.  It is easier to learn Python if you've coded with R.  There are higher learning curves with Stata."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Started\n",
    "There are a few ways to start Jupyter;\n",
    "\n",
    "A. Use the Anaconda interface and click on launch:\n",
    "\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/anaconda.png\">\n",
    "\n",
    "Or\n",
    "\n",
    "B. For Mac: go to your terminal and type:  jupyter notebook  \n",
    "\n",
    "For Windows: go to your control prompt (search for cmd) and type: jupyter notebook  \n",
    "\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/terminal.png\">\n",
    "\n",
    "It will open a new screen in your browser of choice (your default browse) that will have your existing directory (ak folders/files). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Where ever you open a new notebook is where the notebook will be saved.\n",
    "To create a new notebook you can:\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/newdoc.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will open up a new notebook and automatically save your changes as you code (you can even see when it last saved above)\n",
    "\n",
    "Let's change the name of your new file, it's easy!\n",
    "\n",
    "Just click on the file name (untitled at the moment) and type in your new file new:\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/filename.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter Notebook is verstaile - you can code in html, markdown, python, R and LaTex all in the same place!\n",
    "\n",
    "To switch out of python code and use markdown and html (which is useful for creating slides), just change the coding language like this:\n",
    "\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/changecode.png\">\n",
    "\n",
    "Only the box that we are working in changes the language environment.\n",
    "\n",
    "We can also use LaTex to code directly by using $ on each side of the LaTex code\n",
    "\n",
    "For example:\n",
    "\n",
    "$Y = \\frac{5}{8}X+\\beta{X_1}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To close a python document you can shut down the python notebook by going to your localhost tree root (ie the directory that popped up in your browser, it should be something like http://localhost:8888/tree#running), click on the 'running' tab and then click 'shutdown' like this:\n",
    "\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/close.png\">\n",
    "\n",
    "Otherwise it will keep running (even if you exit the browser)\n",
    "\n",
    "You can also just close your terminal or control prompt and the process should terminate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#To comment out sections for documentation use the hashtag\n",
    "#(ALWAYS document: You will forget what your code means after a few weeks. Even the most experience programmers do this)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Command Boxes\n",
    "To run a box(ie a series of commands) the keystrokes are: shift-enter\n",
    "\n",
    "The entire box of code runs when you hit shift-enter (this box that I am typing within, for example)\n",
    "\n",
    "You can not run code line by line by highlighting it (as you would in R or stata)\n",
    "\n",
    "I usually hashtag out lines of code or create new boxes; often you want boxed data to run functions that you may define or a series of commands to create a final table or graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Packages\n",
    "Python uses pacakges (like LaTex and R)\n",
    "\n",
    "We will cover some of the basic statistical packages for Python\n",
    "\n",
    "Importing packages is easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd  #This package is for managing dataframes\n",
    "import math #This is math package\n",
    "import numpy as np #This is a matlab like package\n",
    "import scipy as sp #This is a statistics package\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt #This is for figures\n",
    "%matplotlib inline\n",
    "import seaborn as sns #This is for prettier figures\n",
    "import networkx as nx #This is a networks package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We won't cover most of these - but, it may be helpful for you in the future.\n",
    "\n",
    "Let's start with manipulating dataframes (pandas)!\n",
    "\n",
    "We'll start by importing a csv file (I suggest always using CSV; ie always convert your data into a csv copy; be sure to use UTF-8 csv for it to be compatible with pandas)\n",
    "\n",
    "You can change your working directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mkaltenberg\n"
     ]
    }
   ],
   "source": [
    "cd /Users/mkaltenberg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create an object wjp that is a DataFrame by reading a csv file\n",
    "\n",
    "The object wjp is temporarily saved throughout yousession, you can also redefine the object as you code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wjp = pd.read_csv('/Users/mkaltenberg/wjp.csv') #using the full filename\n",
    "ineq = pd.read_csv('ineq.csv') #I changed the directory so it is pulling from that folder I described above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you just want to view an ouput, don't assign an object name to it and the output will show you the outpu without temporarily saving it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>coun3</th>\n",
       "      <th>year</th>\n",
       "      <th>atkinson2</th>\n",
       "      <th>gini</th>\n",
       "      <th>m2mratio</th>\n",
       "      <th>palmaratio</th>\n",
       "      <th>income1</th>\n",
       "      <th>income2</th>\n",
       "      <th>income3</th>\n",
       "      <th>...</th>\n",
       "      <th>population</th>\n",
       "      <th>databasesource</th>\n",
       "      <th>sharetop1</th>\n",
       "      <th>sharetop5</th>\n",
       "      <th>source1</th>\n",
       "      <th>sqcoeffvariation</th>\n",
       "      <th>surveysource2</th>\n",
       "      <th>surveyyears</th>\n",
       "      <th>consumptionsurvey</th>\n",
       "      <th>theil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1.548593</td>\n",
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       "      <td>17.995520</td>\n",
       "      <td>30.631360</td>\n",
       "      <td>39.812260</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1962</td>\n",
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       "      <td>0.368112</td>\n",
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       "      <td>17.896650</td>\n",
       "      <td>30.463050</td>\n",
       "      <td>39.593510</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1963</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>17.822540</td>\n",
       "      <td>30.336910</td>\n",
       "      <td>39.429560</td>\n",
       "      <td>...</td>\n",
       "      <td>9531555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1964</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>17.748430</td>\n",
       "      <td>30.210770</td>\n",
       "      <td>39.265610</td>\n",
       "      <td>...</td>\n",
       "      <td>9728645.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1965</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
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       "      <td>17.500200</td>\n",
       "      <td>29.788230</td>\n",
       "      <td>38.716430</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1967</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>17.549430</td>\n",
       "      <td>29.872030</td>\n",
       "      <td>38.825340</td>\n",
       "      <td>...</td>\n",
       "      <td>10368600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1968</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>17.721120</td>\n",
       "      <td>30.164280</td>\n",
       "      <td>39.205180</td>\n",
       "      <td>...</td>\n",
       "      <td>10599790.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>17.572620</td>\n",
       "      <td>29.911500</td>\n",
       "      <td>38.876640</td>\n",
       "      <td>...</td>\n",
       "      <td>10849510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>17.473760</td>\n",
       "      <td>29.743220</td>\n",
       "      <td>38.657930</td>\n",
       "      <td>...</td>\n",
       "      <td>11121097.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107877</td>\n",
       "      <td>0.240413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1971</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>16.246850</td>\n",
       "      <td>27.654820</td>\n",
       "      <td>35.943580</td>\n",
       "      <td>...</td>\n",
       "      <td>11412821.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>15.345400</td>\n",
       "      <td>26.120400</td>\n",
       "      <td>33.949270</td>\n",
       "      <td>...</td>\n",
       "      <td>11716896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1973</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261509</td>\n",
       "      <td>16.542760</td>\n",
       "      <td>28.158520</td>\n",
       "      <td>36.598260</td>\n",
       "      <td>...</td>\n",
       "      <td>12022514.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1974</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>17.304120</td>\n",
       "      <td>29.454470</td>\n",
       "      <td>38.282630</td>\n",
       "      <td>...</td>\n",
       "      <td>12315553.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1975</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>17.129240</td>\n",
       "      <td>29.156800</td>\n",
       "      <td>37.895730</td>\n",
       "      <td>...</td>\n",
       "      <td>12582954.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>16.894170</td>\n",
       "      <td>28.756660</td>\n",
       "      <td>37.375670</td>\n",
       "      <td>...</td>\n",
       "      <td>12831361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>17.944440</td>\n",
       "      <td>30.544400</td>\n",
       "      <td>39.699230</td>\n",
       "      <td>...</td>\n",
       "      <td>13056499.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>18.163990</td>\n",
       "      <td>30.918110</td>\n",
       "      <td>40.184950</td>\n",
       "      <td>...</td>\n",
       "      <td>13222547.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1979</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>17.629670</td>\n",
       "      <td>30.008610</td>\n",
       "      <td>39.002860</td>\n",
       "      <td>...</td>\n",
       "      <td>13283279.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>17.126380</td>\n",
       "      <td>29.151920</td>\n",
       "      <td>37.889390</td>\n",
       "      <td>...</td>\n",
       "      <td>13211412.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107877</td>\n",
       "      <td>0.240413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>17.671980</td>\n",
       "      <td>30.080620</td>\n",
       "      <td>39.096450</td>\n",
       "      <td>...</td>\n",
       "      <td>12996923.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1982</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>18.441500</td>\n",
       "      <td>31.390490</td>\n",
       "      <td>40.798900</td>\n",
       "      <td>...</td>\n",
       "      <td>12667001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1983</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>19.848220</td>\n",
       "      <td>33.784950</td>\n",
       "      <td>43.911040</td>\n",
       "      <td>...</td>\n",
       "      <td>12279095.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>20.751440</td>\n",
       "      <td>35.322380</td>\n",
       "      <td>45.909280</td>\n",
       "      <td>...</td>\n",
       "      <td>11912510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>21.310110</td>\n",
       "      <td>36.273330</td>\n",
       "      <td>47.145240</td>\n",
       "      <td>...</td>\n",
       "      <td>11630498.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107877</td>\n",
       "      <td>0.240413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1986</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>22.371400</td>\n",
       "      <td>38.079830</td>\n",
       "      <td>49.493190</td>\n",
       "      <td>...</td>\n",
       "      <td>11438949.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>20.289340</td>\n",
       "      <td>34.535820</td>\n",
       "      <td>44.886960</td>\n",
       "      <td>...</td>\n",
       "      <td>11337932.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261511</td>\n",
       "      <td>18.583140</td>\n",
       "      <td>31.631570</td>\n",
       "      <td>41.112250</td>\n",
       "      <td>...</td>\n",
       "      <td>11375768.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1989</td>\n",
       "      <td>0.438471</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>1.548593</td>\n",
       "      <td>2.261510</td>\n",
       "      <td>16.832440</td>\n",
       "      <td>28.651600</td>\n",
       "      <td>37.239120</td>\n",
       "      <td>...</td>\n",
       "      <td>11608351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8809</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1986</td>\n",
       "      <td>0.924070</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>4.584496</td>\n",
       "      <td>22.915530</td>\n",
       "      <td>0.539933</td>\n",
       "      <td>1.916652</td>\n",
       "      <td>3.726722</td>\n",
       "      <td>...</td>\n",
       "      <td>7242496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.815408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.088820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8810</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.924070</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>4.584496</td>\n",
       "      <td>22.915530</td>\n",
       "      <td>0.580900</td>\n",
       "      <td>2.062078</td>\n",
       "      <td>4.009485</td>\n",
       "      <td>...</td>\n",
       "      <td>7469270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.815409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.088820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8811</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.924070</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>4.584496</td>\n",
       "      <td>22.915530</td>\n",
       "      <td>0.605736</td>\n",
       "      <td>2.150240</td>\n",
       "      <td>4.180908</td>\n",
       "      <td>...</td>\n",
       "      <td>7696070.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.815409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.088820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8812</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1989</td>\n",
       "      <td>0.924070</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>4.584496</td>\n",
       "      <td>22.915530</td>\n",
       "      <td>0.591989</td>\n",
       "      <td>2.101441</td>\n",
       "      <td>4.086024</td>\n",
       "      <td>...</td>\n",
       "      <td>7921028.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.815408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.088820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8813</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.924070</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>4.584496</td>\n",
       "      <td>22.915530</td>\n",
       "      <td>0.540349</td>\n",
       "      <td>1.918128</td>\n",
       "      <td>3.729590</td>\n",
       "      <td>...</td>\n",
       "      <td>8143142.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.371539</td>\n",
       "      <td>0.556715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.815409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.088820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8814</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.924070</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>4.584496</td>\n",
       "      <td>22.915530</td>\n",
       "      <td>0.560304</td>\n",
       "      <td>1.988964</td>\n",
       "      <td>3.867325</td>\n",
       "      <td>...</td>\n",
       "      <td>8361381.0</td>\n",
       "      <td>wiid3b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deininger &amp; Squire, World Bank 2004</td>\n",
       "      <td>1.815409</td>\n",
       "      <td>Social Dimensions of Adjustment Priority Survey</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.088820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8815</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.836563</td>\n",
       "      <td>0.729179</td>\n",
       "      <td>3.324160</td>\n",
       "      <td>12.568560</td>\n",
       "      <td>1.498168</td>\n",
       "      <td>3.721243</td>\n",
       "      <td>6.190398</td>\n",
       "      <td>...</td>\n",
       "      <td>8576987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.486264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.914440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8816</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.760195</td>\n",
       "      <td>0.623741</td>\n",
       "      <td>2.607362</td>\n",
       "      <td>8.147513</td>\n",
       "      <td>2.489938</td>\n",
       "      <td>5.599805</td>\n",
       "      <td>8.768405</td>\n",
       "      <td>...</td>\n",
       "      <td>8794061.0</td>\n",
       "      <td>wiid3b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deininger &amp; Squire, World Bank 2004</td>\n",
       "      <td>1.191884</td>\n",
       "      <td>Social Dimensions of Adjustment Priority Survey</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.760146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8817</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.757305</td>\n",
       "      <td>0.625283</td>\n",
       "      <td>2.632384</td>\n",
       "      <td>8.232767</td>\n",
       "      <td>2.463693</td>\n",
       "      <td>5.260434</td>\n",
       "      <td>8.211821</td>\n",
       "      <td>...</td>\n",
       "      <td>9018229.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.202013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.764765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8818</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.754723</td>\n",
       "      <td>0.626826</td>\n",
       "      <td>2.657890</td>\n",
       "      <td>8.319165</td>\n",
       "      <td>2.573429</td>\n",
       "      <td>5.225305</td>\n",
       "      <td>8.131490</td>\n",
       "      <td>...</td>\n",
       "      <td>9253527.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.271502</td>\n",
       "      <td>0.443927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.212193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.769420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8819</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.752429</td>\n",
       "      <td>0.628368</td>\n",
       "      <td>2.683894</td>\n",
       "      <td>8.406731</td>\n",
       "      <td>2.768225</td>\n",
       "      <td>5.353271</td>\n",
       "      <td>8.304008</td>\n",
       "      <td>...</td>\n",
       "      <td>9502346.0</td>\n",
       "      <td>wiid3b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deininger &amp; Squire, World Bank 2004</td>\n",
       "      <td>1.222424</td>\n",
       "      <td>Living Conditions Monitoring Survey I</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.774111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8820</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.770780</td>\n",
       "      <td>0.672846</td>\n",
       "      <td>2.741592</td>\n",
       "      <td>8.723086</td>\n",
       "      <td>2.392149</td>\n",
       "      <td>5.187355</td>\n",
       "      <td>8.161174</td>\n",
       "      <td>...</td>\n",
       "      <td>9763742.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.217625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.778907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8821</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.791540</td>\n",
       "      <td>0.653514</td>\n",
       "      <td>2.801826</td>\n",
       "      <td>9.067028</td>\n",
       "      <td>1.926362</td>\n",
       "      <td>4.813454</td>\n",
       "      <td>7.688674</td>\n",
       "      <td>...</td>\n",
       "      <td>10034412.0</td>\n",
       "      <td>wiid3b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deininger &amp; Squire, World Bank 2004</td>\n",
       "      <td>1.213223</td>\n",
       "      <td>Living Conditions Monitoring Survey II</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.784185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8822</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>1999</td>\n",
       "      <td>0.765138</td>\n",
       "      <td>0.645662</td>\n",
       "      <td>2.655669</td>\n",
       "      <td>8.154582</td>\n",
       "      <td>2.356841</td>\n",
       "      <td>5.425216</td>\n",
       "      <td>8.463896</td>\n",
       "      <td>...</td>\n",
       "      <td>10309310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.153944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.749781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8823</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.740142</td>\n",
       "      <td>0.610577</td>\n",
       "      <td>2.524006</td>\n",
       "      <td>7.379541</td>\n",
       "      <td>2.784217</td>\n",
       "      <td>6.018188</td>\n",
       "      <td>9.204302</td>\n",
       "      <td>...</td>\n",
       "      <td>10585220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.244717</td>\n",
       "      <td>0.417113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.096217</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.716388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8824</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.716215</td>\n",
       "      <td>0.597737</td>\n",
       "      <td>2.404781</td>\n",
       "      <td>6.713046</td>\n",
       "      <td>3.267626</td>\n",
       "      <td>6.719506</td>\n",
       "      <td>10.103980</td>\n",
       "      <td>...</td>\n",
       "      <td>10861238.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.040041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.683956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8825</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.693135</td>\n",
       "      <td>0.584896</td>\n",
       "      <td>2.296311</td>\n",
       "      <td>6.133781</td>\n",
       "      <td>3.747064</td>\n",
       "      <td>7.399247</td>\n",
       "      <td>10.964120</td>\n",
       "      <td>...</td>\n",
       "      <td>11139978.0</td>\n",
       "      <td>Povcalnet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.985417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.652442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8826</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.741085</td>\n",
       "      <td>0.604572</td>\n",
       "      <td>2.463897</td>\n",
       "      <td>7.151001</td>\n",
       "      <td>3.180888</td>\n",
       "      <td>7.063526</td>\n",
       "      <td>10.942210</td>\n",
       "      <td>...</td>\n",
       "      <td>11426006.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.045192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.694959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8827</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.788300</td>\n",
       "      <td>0.610240</td>\n",
       "      <td>2.629960</td>\n",
       "      <td>8.294333</td>\n",
       "      <td>2.560285</td>\n",
       "      <td>6.687566</td>\n",
       "      <td>10.901540</td>\n",
       "      <td>...</td>\n",
       "      <td>11725635.0</td>\n",
       "      <td>Povcalnet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.098578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.733857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8828</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.788533</td>\n",
       "      <td>0.612053</td>\n",
       "      <td>2.637606</td>\n",
       "      <td>8.330371</td>\n",
       "      <td>2.585275</td>\n",
       "      <td>6.741440</td>\n",
       "      <td>10.981040</td>\n",
       "      <td>...</td>\n",
       "      <td>12043591.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.234249</td>\n",
       "      <td>0.412315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.103625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.736127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8829</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.788784</td>\n",
       "      <td>0.614007</td>\n",
       "      <td>2.645841</td>\n",
       "      <td>8.369129</td>\n",
       "      <td>2.621342</td>\n",
       "      <td>6.823116</td>\n",
       "      <td>11.105040</td>\n",
       "      <td>...</td>\n",
       "      <td>12381509.0</td>\n",
       "      <td>Povcalnet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.109046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.738562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8830</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.786687</td>\n",
       "      <td>0.618513</td>\n",
       "      <td>2.655124</td>\n",
       "      <td>8.378014</td>\n",
       "      <td>2.691921</td>\n",
       "      <td>6.925332</td>\n",
       "      <td>11.208570</td>\n",
       "      <td>...</td>\n",
       "      <td>12738676.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.118187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.741810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8831</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.784435</td>\n",
       "      <td>0.623543</td>\n",
       "      <td>2.665373</td>\n",
       "      <td>8.387691</td>\n",
       "      <td>2.746080</td>\n",
       "      <td>6.977175</td>\n",
       "      <td>11.224220</td>\n",
       "      <td>...</td>\n",
       "      <td>13114579.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.128279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.745400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8832</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.782012</td>\n",
       "      <td>0.629189</td>\n",
       "      <td>2.676749</td>\n",
       "      <td>8.398272</td>\n",
       "      <td>2.835660</td>\n",
       "      <td>7.109570</td>\n",
       "      <td>11.362000</td>\n",
       "      <td>...</td>\n",
       "      <td>13507849.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.139480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.749389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8833</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.779398</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>2.689448</td>\n",
       "      <td>8.409883</td>\n",
       "      <td>2.953956</td>\n",
       "      <td>7.301502</td>\n",
       "      <td>11.584950</td>\n",
       "      <td>...</td>\n",
       "      <td>13917439.0</td>\n",
       "      <td>Povcalnet</td>\n",
       "      <td>0.249741</td>\n",
       "      <td>0.426467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.753847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8834</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.779398</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>2.689448</td>\n",
       "      <td>8.409883</td>\n",
       "      <td>3.047879</td>\n",
       "      <td>7.533658</td>\n",
       "      <td>11.953300</td>\n",
       "      <td>...</td>\n",
       "      <td>14343526.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8835</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.779398</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>2.689448</td>\n",
       "      <td>8.409883</td>\n",
       "      <td>3.155548</td>\n",
       "      <td>7.799790</td>\n",
       "      <td>12.375560</td>\n",
       "      <td>...</td>\n",
       "      <td>14786581.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8836</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.779398</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>2.689448</td>\n",
       "      <td>8.409883</td>\n",
       "      <td>3.265904</td>\n",
       "      <td>8.072563</td>\n",
       "      <td>12.808350</td>\n",
       "      <td>...</td>\n",
       "      <td>15246086.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.249741</td>\n",
       "      <td>0.426467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8837</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.779398</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>2.689448</td>\n",
       "      <td>8.409883</td>\n",
       "      <td>3.357206</td>\n",
       "      <td>8.298241</td>\n",
       "      <td>13.166430</td>\n",
       "      <td>...</td>\n",
       "      <td>15721343.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8838</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.779398</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>2.689448</td>\n",
       "      <td>8.409883</td>\n",
       "      <td>3.469737</td>\n",
       "      <td>8.576392</td>\n",
       "      <td>13.607760</td>\n",
       "      <td>...</td>\n",
       "      <td>15808000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.249741</td>\n",
       "      <td>0.426467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8839 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country coun3  year  atkinson2      gini  m2mratio  palmaratio  \\\n",
       "0     Afghanistan   AFG  1960   0.438471  0.368112  1.548593    2.261509   \n",
       "1     Afghanistan   AFG  1961   0.438471  0.368112  1.548593    2.261509   \n",
       "2     Afghanistan   AFG  1962   0.438471  0.368112  1.548593    2.261509   \n",
       "3     Afghanistan   AFG  1963   0.438471  0.368112  1.548593    2.261509   \n",
       "4     Afghanistan   AFG  1964   0.438471  0.368112  1.548593    2.261509   \n",
       "5     Afghanistan   AFG  1965   0.438471  0.368112  1.548593    2.261509   \n",
       "6     Afghanistan   AFG  1966   0.438471  0.368112  1.548593    2.261509   \n",
       "7     Afghanistan   AFG  1967   0.438471  0.368112  1.548593    2.261509   \n",
       "8     Afghanistan   AFG  1968   0.438471  0.368112  1.548593    2.261509   \n",
       "9     Afghanistan   AFG  1969   0.438471  0.368112  1.548593    2.261509   \n",
       "10    Afghanistan   AFG  1970   0.438471  0.368112  1.548593    2.261509   \n",
       "11    Afghanistan   AFG  1971   0.438471  0.368112  1.548593    2.261510   \n",
       "12    Afghanistan   AFG  1972   0.438471  0.368112  1.548593    2.261509   \n",
       "13    Afghanistan   AFG  1973   0.438471  0.368112  1.548593    2.261509   \n",
       "14    Afghanistan   AFG  1974   0.438471  0.368112  1.548593    2.261510   \n",
       "15    Afghanistan   AFG  1975   0.438471  0.368112  1.548593    2.261510   \n",
       "16    Afghanistan   AFG  1976   0.438471  0.368112  1.548593    2.261510   \n",
       "17    Afghanistan   AFG  1977   0.438471  0.368112  1.548593    2.261510   \n",
       "18    Afghanistan   AFG  1978   0.438471  0.368112  1.548593    2.261510   \n",
       "19    Afghanistan   AFG  1979   0.438471  0.368112  1.548593    2.261510   \n",
       "20    Afghanistan   AFG  1980   0.438471  0.368112  1.548593    2.261510   \n",
       "21    Afghanistan   AFG  1981   0.438471  0.368112  1.548593    2.261510   \n",
       "22    Afghanistan   AFG  1982   0.438471  0.368112  1.548593    2.261510   \n",
       "23    Afghanistan   AFG  1983   0.438471  0.368112  1.548593    2.261510   \n",
       "24    Afghanistan   AFG  1984   0.438471  0.368112  1.548593    2.261510   \n",
       "25    Afghanistan   AFG  1985   0.438471  0.368112  1.548593    2.261510   \n",
       "26    Afghanistan   AFG  1986   0.438471  0.368112  1.548593    2.261510   \n",
       "27    Afghanistan   AFG  1987   0.438471  0.368112  1.548593    2.261510   \n",
       "28    Afghanistan   AFG  1988   0.438471  0.368112  1.548593    2.261511   \n",
       "29    Afghanistan   AFG  1989   0.438471  0.368112  1.548593    2.261510   \n",
       "...           ...   ...   ...        ...       ...       ...         ...   \n",
       "8809       Zambia   ZMB  1986   0.924070  0.757830  4.584496   22.915530   \n",
       "8810       Zambia   ZMB  1987   0.924070  0.757830  4.584496   22.915530   \n",
       "8811       Zambia   ZMB  1988   0.924070  0.757830  4.584496   22.915530   \n",
       "8812       Zambia   ZMB  1989   0.924070  0.757830  4.584496   22.915530   \n",
       "8813       Zambia   ZMB  1990   0.924070  0.757830  4.584496   22.915530   \n",
       "8814       Zambia   ZMB  1991   0.924070  0.757830  4.584496   22.915530   \n",
       "8815       Zambia   ZMB  1992   0.836563  0.729179  3.324160   12.568560   \n",
       "8816       Zambia   ZMB  1993   0.760195  0.623741  2.607362    8.147513   \n",
       "8817       Zambia   ZMB  1994   0.757305  0.625283  2.632384    8.232767   \n",
       "8818       Zambia   ZMB  1995   0.754723  0.626826  2.657890    8.319165   \n",
       "8819       Zambia   ZMB  1996   0.752429  0.628368  2.683894    8.406731   \n",
       "8820       Zambia   ZMB  1997   0.770780  0.672846  2.741592    8.723086   \n",
       "8821       Zambia   ZMB  1998   0.791540  0.653514  2.801826    9.067028   \n",
       "8822       Zambia   ZMB  1999   0.765138  0.645662  2.655669    8.154582   \n",
       "8823       Zambia   ZMB  2000   0.740142  0.610577  2.524006    7.379541   \n",
       "8824       Zambia   ZMB  2001   0.716215  0.597737  2.404781    6.713046   \n",
       "8825       Zambia   ZMB  2002   0.693135  0.584896  2.296311    6.133781   \n",
       "8826       Zambia   ZMB  2003   0.741085  0.604572  2.463897    7.151001   \n",
       "8827       Zambia   ZMB  2004   0.788300  0.610240  2.629960    8.294333   \n",
       "8828       Zambia   ZMB  2005   0.788533  0.612053  2.637606    8.330371   \n",
       "8829       Zambia   ZMB  2006   0.788784  0.614007  2.645841    8.369129   \n",
       "8830       Zambia   ZMB  2007   0.786687  0.618513  2.655124    8.378014   \n",
       "8831       Zambia   ZMB  2008   0.784435  0.623543  2.665373    8.387691   \n",
       "8832       Zambia   ZMB  2009   0.782012  0.629189  2.676749    8.398272   \n",
       "8833       Zambia   ZMB  2010   0.779398  0.635562  2.689448    8.409883   \n",
       "8834       Zambia   ZMB  2011   0.779398  0.635562  2.689448    8.409883   \n",
       "8835       Zambia   ZMB  2012   0.779398  0.635562  2.689448    8.409883   \n",
       "8836       Zambia   ZMB  2013   0.779398  0.635562  2.689448    8.409883   \n",
       "8837       Zambia   ZMB  2014   0.779398  0.635562  2.689448    8.409883   \n",
       "8838       Zambia   ZMB  2015   0.779398  0.635562  2.689448    8.409883   \n",
       "\n",
       "        income1    income2    income3    ...     population  databasesource  \\\n",
       "0     18.218770  31.011350  40.306140    ...      8994793.0             NaN   \n",
       "1     17.995520  30.631360  39.812260    ...      9164945.0             NaN   \n",
       "2     17.896650  30.463050  39.593510    ...      9343772.0             NaN   \n",
       "3     17.822540  30.336910  39.429560    ...      9531555.0             NaN   \n",
       "4     17.748430  30.210770  39.265610    ...      9728645.0             NaN   \n",
       "5     17.748430  30.210770  39.265610    ...      9935358.0             NaN   \n",
       "6     17.500200  29.788230  38.716430    ...     10148841.0             NaN   \n",
       "7     17.549430  29.872030  38.825340    ...     10368600.0             NaN   \n",
       "8     17.721120  30.164280  39.205180    ...     10599790.0             NaN   \n",
       "9     17.572620  29.911500  38.876640    ...     10849510.0             NaN   \n",
       "10    17.473760  29.743220  38.657930    ...     11121097.0             NaN   \n",
       "11    16.246850  27.654820  35.943580    ...     11412821.0             NaN   \n",
       "12    15.345400  26.120400  33.949270    ...     11716896.0             NaN   \n",
       "13    16.542760  28.158520  36.598260    ...     12022514.0             NaN   \n",
       "14    17.304120  29.454470  38.282630    ...     12315553.0             NaN   \n",
       "15    17.129240  29.156800  37.895730    ...     12582954.0             NaN   \n",
       "16    16.894170  28.756660  37.375670    ...     12831361.0             NaN   \n",
       "17    17.944440  30.544400  39.699230    ...     13056499.0             NaN   \n",
       "18    18.163990  30.918110  40.184950    ...     13222547.0             NaN   \n",
       "19    17.629670  30.008610  39.002860    ...     13283279.0             NaN   \n",
       "20    17.126380  29.151920  37.889390    ...     13211412.0             NaN   \n",
       "21    17.671980  30.080620  39.096450    ...     12996923.0             NaN   \n",
       "22    18.441500  31.390490  40.798900    ...     12667001.0             NaN   \n",
       "23    19.848220  33.784950  43.911040    ...     12279095.0             NaN   \n",
       "24    20.751440  35.322380  45.909280    ...     11912510.0             NaN   \n",
       "25    21.310110  36.273330  47.145240    ...     11630498.0             NaN   \n",
       "26    22.371400  38.079830  49.493190    ...     11438949.0             NaN   \n",
       "27    20.289340  34.535820  44.886960    ...     11337932.0             NaN   \n",
       "28    18.583140  31.631570  41.112250    ...     11375768.0             NaN   \n",
       "29    16.832440  28.651600  37.239120    ...     11608351.0             NaN   \n",
       "...         ...        ...        ...    ...            ...             ...   \n",
       "8809   0.539933   1.916652   3.726722    ...      7242496.0             NaN   \n",
       "8810   0.580900   2.062078   4.009485    ...      7469270.0             NaN   \n",
       "8811   0.605736   2.150240   4.180908    ...      7696070.0             NaN   \n",
       "8812   0.591989   2.101441   4.086024    ...      7921028.0             NaN   \n",
       "8813   0.540349   1.918128   3.729590    ...      8143142.0             NaN   \n",
       "8814   0.560304   1.988964   3.867325    ...      8361381.0          wiid3b   \n",
       "8815   1.498168   3.721243   6.190398    ...      8576987.0             NaN   \n",
       "8816   2.489938   5.599805   8.768405    ...      8794061.0          wiid3b   \n",
       "8817   2.463693   5.260434   8.211821    ...      9018229.0             NaN   \n",
       "8818   2.573429   5.225305   8.131490    ...      9253527.0             NaN   \n",
       "8819   2.768225   5.353271   8.304008    ...      9502346.0          wiid3b   \n",
       "8820   2.392149   5.187355   8.161174    ...      9763742.0             NaN   \n",
       "8821   1.926362   4.813454   7.688674    ...     10034412.0          wiid3b   \n",
       "8822   2.356841   5.425216   8.463896    ...     10309310.0             NaN   \n",
       "8823   2.784217   6.018188   9.204302    ...     10585220.0             NaN   \n",
       "8824   3.267626   6.719506  10.103980    ...     10861238.0             NaN   \n",
       "8825   3.747064   7.399247  10.964120    ...     11139978.0       Povcalnet   \n",
       "8826   3.180888   7.063526  10.942210    ...     11426006.0             NaN   \n",
       "8827   2.560285   6.687566  10.901540    ...     11725635.0       Povcalnet   \n",
       "8828   2.585275   6.741440  10.981040    ...     12043591.0             NaN   \n",
       "8829   2.621342   6.823116  11.105040    ...     12381509.0       Povcalnet   \n",
       "8830   2.691921   6.925332  11.208570    ...     12738676.0             NaN   \n",
       "8831   2.746080   6.977175  11.224220    ...     13114579.0             NaN   \n",
       "8832   2.835660   7.109570  11.362000    ...     13507849.0             NaN   \n",
       "8833   2.953956   7.301502  11.584950    ...     13917439.0       Povcalnet   \n",
       "8834   3.047879   7.533658  11.953300    ...     14343526.0             NaN   \n",
       "8835   3.155548   7.799790  12.375560    ...     14786581.0             NaN   \n",
       "8836   3.265904   8.072563  12.808350    ...     15246086.0             NaN   \n",
       "8837   3.357206   8.298241  13.166430    ...     15721343.0             NaN   \n",
       "8838   3.469737   8.576392  13.607760    ...     15808000.0             NaN   \n",
       "\n",
       "      sharetop1  sharetop5                              source1  \\\n",
       "0      0.107877   0.240413                                  NaN   \n",
       "1           NaN        NaN                                  NaN   \n",
       "2           NaN        NaN                                  NaN   \n",
       "3           NaN        NaN                                  NaN   \n",
       "4           NaN        NaN                                  NaN   \n",
       "5           NaN        NaN                                  NaN   \n",
       "6           NaN        NaN                                  NaN   \n",
       "7           NaN        NaN                                  NaN   \n",
       "8           NaN        NaN                                  NaN   \n",
       "9           NaN        NaN                                  NaN   \n",
       "10     0.107877   0.240413                                  NaN   \n",
       "11          NaN        NaN                                  NaN   \n",
       "12          NaN        NaN                                  NaN   \n",
       "13          NaN        NaN                                  NaN   \n",
       "14          NaN        NaN                                  NaN   \n",
       "15          NaN        NaN                                  NaN   \n",
       "16          NaN        NaN                                  NaN   \n",
       "17          NaN        NaN                                  NaN   \n",
       "18          NaN        NaN                                  NaN   \n",
       "19          NaN        NaN                                  NaN   \n",
       "20     0.107877   0.240413                                  NaN   \n",
       "21          NaN        NaN                                  NaN   \n",
       "22          NaN        NaN                                  NaN   \n",
       "23          NaN        NaN                                  NaN   \n",
       "24          NaN        NaN                                  NaN   \n",
       "25     0.107877   0.240413                                  NaN   \n",
       "26          NaN        NaN                                  NaN   \n",
       "27          NaN        NaN                                  NaN   \n",
       "28          NaN        NaN                                  NaN   \n",
       "29          NaN        NaN                                  NaN   \n",
       "...         ...        ...                                  ...   \n",
       "8809        NaN        NaN                                  NaN   \n",
       "8810        NaN        NaN                                  NaN   \n",
       "8811        NaN        NaN                                  NaN   \n",
       "8812        NaN        NaN                                  NaN   \n",
       "8813   0.371539   0.556715                                  NaN   \n",
       "8814        NaN        NaN  Deininger & Squire, World Bank 2004   \n",
       "8815        NaN        NaN                                  NaN   \n",
       "8816        NaN        NaN  Deininger & Squire, World Bank 2004   \n",
       "8817        NaN        NaN                                  NaN   \n",
       "8818   0.271502   0.443927                                  NaN   \n",
       "8819        NaN        NaN  Deininger & Squire, World Bank 2004   \n",
       "8820        NaN        NaN                                  NaN   \n",
       "8821        NaN        NaN  Deininger & Squire, World Bank 2004   \n",
       "8822        NaN        NaN                                  NaN   \n",
       "8823   0.244717   0.417113                                  NaN   \n",
       "8824        NaN        NaN                                  NaN   \n",
       "8825        NaN        NaN                                  NaN   \n",
       "8826        NaN        NaN                                  NaN   \n",
       "8827        NaN        NaN                                  NaN   \n",
       "8828   0.234249   0.412315                                  NaN   \n",
       "8829        NaN        NaN                                  NaN   \n",
       "8830        NaN        NaN                                  NaN   \n",
       "8831        NaN        NaN                                  NaN   \n",
       "8832        NaN        NaN                                  NaN   \n",
       "8833   0.249741   0.426467                                  NaN   \n",
       "8834        NaN        NaN                                  NaN   \n",
       "8835        NaN        NaN                                  NaN   \n",
       "8836   0.249741   0.426467                                  NaN   \n",
       "8837        NaN        NaN                                  NaN   \n",
       "8838   0.249741   0.426467                                  NaN   \n",
       "\n",
       "      sqcoeffvariation                                    surveysource2  \\\n",
       "0             0.402499                                              NaN   \n",
       "1             0.402499                                              NaN   \n",
       "2             0.402499                                              NaN   \n",
       "3             0.402499                                              NaN   \n",
       "4             0.402499                                              NaN   \n",
       "5             0.402499                                              NaN   \n",
       "6             0.402499                                              NaN   \n",
       "7             0.402499                                              NaN   \n",
       "8             0.402499                                              NaN   \n",
       "9             0.402499                                              NaN   \n",
       "10            0.402499                                              NaN   \n",
       "11            0.402499                                              NaN   \n",
       "12            0.402499                                              NaN   \n",
       "13            0.402499                                              NaN   \n",
       "14            0.402499                                              NaN   \n",
       "15            0.402499                                              NaN   \n",
       "16            0.402499                                              NaN   \n",
       "17            0.402499                                              NaN   \n",
       "18            0.402499                                              NaN   \n",
       "19            0.402499                                              NaN   \n",
       "20            0.402499                                              NaN   \n",
       "21            0.402499                                              NaN   \n",
       "22            0.402499                                              NaN   \n",
       "23            0.402499                                              NaN   \n",
       "24            0.402499                                              NaN   \n",
       "25            0.402499                                              NaN   \n",
       "26            0.402499                                              NaN   \n",
       "27            0.402499                                              NaN   \n",
       "28            0.402499                                              NaN   \n",
       "29            0.402499                                              NaN   \n",
       "...                ...                                              ...   \n",
       "8809          1.815408                                              NaN   \n",
       "8810          1.815409                                              NaN   \n",
       "8811          1.815409                                              NaN   \n",
       "8812          1.815408                                              NaN   \n",
       "8813          1.815409                                              NaN   \n",
       "8814          1.815409  Social Dimensions of Adjustment Priority Survey   \n",
       "8815          1.486264                                              NaN   \n",
       "8816          1.191884  Social Dimensions of Adjustment Priority Survey   \n",
       "8817          1.202013                                              NaN   \n",
       "8818          1.212193                                              NaN   \n",
       "8819          1.222424            Living Conditions Monitoring Survey I   \n",
       "8820          1.217625                                              NaN   \n",
       "8821          1.213223           Living Conditions Monitoring Survey II   \n",
       "8822          1.153944                                              NaN   \n",
       "8823          1.096217                                              NaN   \n",
       "8824          1.040041                                              NaN   \n",
       "8825          0.985417                                              NaN   \n",
       "8826          1.045192                                              NaN   \n",
       "8827          1.098578                                              NaN   \n",
       "8828          1.103625                                              NaN   \n",
       "8829          1.109046                                              NaN   \n",
       "8830          1.118187                                              NaN   \n",
       "8831          1.128279                                              NaN   \n",
       "8832          1.139480                                              NaN   \n",
       "8833          1.151980                                              NaN   \n",
       "8834          1.151980                                              NaN   \n",
       "8835          1.151980                                              NaN   \n",
       "8836          1.151980                                              NaN   \n",
       "8837          1.151980                                              NaN   \n",
       "8838          1.151980                                              NaN   \n",
       "\n",
       "      surveyyears  consumptionsurvey     theil  \n",
       "0               0                NaN  0.314756  \n",
       "1               0                NaN  0.314756  \n",
       "2               0                NaN  0.314756  \n",
       "3               0                NaN  0.314756  \n",
       "4               0                NaN  0.314756  \n",
       "5               0                NaN  0.314756  \n",
       "6               0                NaN  0.314756  \n",
       "7               0                NaN  0.314756  \n",
       "8               0                NaN  0.314756  \n",
       "9               0                NaN  0.314756  \n",
       "10              0                NaN  0.314756  \n",
       "11              0                NaN  0.314756  \n",
       "12              0                NaN  0.314756  \n",
       "13              0                NaN  0.314756  \n",
       "14              0                NaN  0.314756  \n",
       "15              0                NaN  0.314756  \n",
       "16              0                NaN  0.314756  \n",
       "17              0                NaN  0.314756  \n",
       "18              0                NaN  0.314756  \n",
       "19              0                NaN  0.314756  \n",
       "20              0                NaN  0.314756  \n",
       "21              0                NaN  0.314756  \n",
       "22              0                NaN  0.314756  \n",
       "23              0                NaN  0.314756  \n",
       "24              0                NaN  0.314756  \n",
       "25              0                NaN  0.314756  \n",
       "26              0                NaN  0.314756  \n",
       "27              0                NaN  0.314756  \n",
       "28              0                NaN  0.314756  \n",
       "29              0                NaN  0.314756  \n",
       "...           ...                ...       ...  \n",
       "8809            0                NaN  1.088820  \n",
       "8810            0                NaN  1.088820  \n",
       "8811            0                NaN  1.088820  \n",
       "8812            0                NaN  1.088820  \n",
       "8813            0                NaN  1.088820  \n",
       "8814            1                0.0  1.088820  \n",
       "8815            0                NaN  0.914440  \n",
       "8816            1                0.0  0.760146  \n",
       "8817            0                NaN  0.764765  \n",
       "8818            0                NaN  0.769420  \n",
       "8819            1                0.0  0.774111  \n",
       "8820            0                NaN  0.778907  \n",
       "8821            1                0.0  0.784185  \n",
       "8822            0                NaN  0.749781  \n",
       "8823            0                NaN  0.716388  \n",
       "8824            0                NaN  0.683956  \n",
       "8825            1                1.0  0.652442  \n",
       "8826            0                NaN  0.694959  \n",
       "8827            1                1.0  0.733857  \n",
       "8828            0                NaN  0.736127  \n",
       "8829            1                1.0  0.738562  \n",
       "8830            0                NaN  0.741810  \n",
       "8831            0                NaN  0.745400  \n",
       "8832            0                NaN  0.749389  \n",
       "8833            1                1.0  0.753847  \n",
       "8834            0                NaN  0.753847  \n",
       "8835            0                NaN  0.753847  \n",
       "8836            0                NaN  0.753847  \n",
       "8837            0                NaN  0.753847  \n",
       "8838            0                NaN  0.753847  \n",
       "\n",
       "[8839 rows x 56 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ineq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need to stop running a function, you can interupt:\n",
    "\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/stop.png\">\n",
    "\n",
    "or restart the kernel if it's frozen\n",
    "\n",
    "Restarting the kernel means all of your saved objects will be lost and you will have to re-run everything\n",
    "\n",
    "<img style=\"float: center; width: 60%; height: 60%;\" src=\"Intro to Python screenshots/terminate.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.indexes.base.Index"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's find out all of the column names\n",
    "i = wjp.columns \n",
    "type(i) #to find out what type of object i is\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#merge datasets together (the real beauty of python, \n",
    "#this is very verstaile; you can also use the functions append and concat for bringing datasets together)\n",
    "data = pd.merge(wjp,ineq, left_on = ['isocode', 'year'], right_on = ['coun3', 'year'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Income Group', 'isocode', 'year', 'id',\n",
       "       'isocode.1', 'factor1',\n",
       "       'f1.2 Government powers are effectively limited by the legislature',\n",
       "       'f1.3 Government powers are effectively limited by the judiciary',\n",
       "       'f1.4 Government powers are effectively limited by independent auditing and review',\n",
       "       'f1.5 Government officials are sanctioned for misconduct',\n",
       "       'f1.6 Government powers are subject to non-governmental checks',\n",
       "       'f1.7 Transition of power is subject to the law', 'factor2',\n",
       "       'f2.1 Government officials in the executive branch do not use public office for private gain',\n",
       "       'f2.2 Government officials in the judicial branch do not use public office for private gain',\n",
       "       'f2.3 Government officials in the police and the military do not use public office for private gain',\n",
       "       'f2.4 Government officials in the legislative branch do not use public office for private gain',\n",
       "       'factor3', 'f3.1 Crime is effectively controlled',\n",
       "       'f3.2 Civil conflict is effectively limited',\n",
       "       'f3.3 People do not resort to violence to redress personal grievances',\n",
       "       'factor4', 'f4.1 Equal treatment and absence of discrimination',\n",
       "       'f4.2 The right to life and security of the person is effectively guaranteed',\n",
       "       'f4.3 Due process of law and rights of the accused',\n",
       "       'f4.4 Freedom of opinion and expression is effectively guaranteed',\n",
       "       'f4.5 Freedom of belief and religion is effectively guaranteed',\n",
       "       'f4.6 Freedom from arbitrary interference with privacy is effectively guaranteed',\n",
       "       'f4.7 Freedom of assembly and association is effectively guaranteed',\n",
       "       'f4.8 Fundamental labor rights are effectively guaranteed', 'factor5',\n",
       "       'f5.1 The laws are publicized and accessible',\n",
       "       'f5.2 The laws are stable',\n",
       "       'f5.3 Right to petition the government and public participation',\n",
       "       'f5.4 Official information is available on request', 'factor6',\n",
       "       'f6.1 Government regulations are effectively enforced',\n",
       "       'f6.2 Government regulations are applied and enforced without improper influence',\n",
       "       'f6.3 Administrative proceedings are conducted without unreasonable delay',\n",
       "       'f6.4 Due process is respected in administrative proceedings',\n",
       "       'f6.5 The Government does not expropriate without adequate compensation',\n",
       "       'factor7', 'f7.1 People can access and afford civil justice',\n",
       "       'f7.2 Civil justice is free of discrimination',\n",
       "       'f7.3 Civil justice is free of corruption',\n",
       "       'f7.4 Civil justice is free of improper government influence',\n",
       "       'f7.5 Civil justice is not subject to unreasonable delays',\n",
       "       'f7.6 Civil justice is effectively enforced',\n",
       "       'f7.7 ADRs are accessible, impartial, and effective', 'factor8',\n",
       "       'f8.1 Criminal investigation system is effective',\n",
       "       'f8.2 Criminal adjudication system is timely and effective',\n",
       "       'f8.3 Correctional system is effective in reducing criminal behavior',\n",
       "       'f8.4 Criminal system is impartial',\n",
       "       'f8.5 Criminal system is free of corruption',\n",
       "       'f8.6 Criminal system is free of improper government influence',\n",
       "       'f8.7 Due process of law and rights of the accused'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm lazy, so using the object i, I copy and paste the list of variables I am interested in from the output I get here:\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#filter the data to include only the variable that you are interested in\n",
    "c = data[['Country', 'Region','Income Group', 'year', 'isocode', 'factor1',\n",
    "       'f1.2 Government powers are effectively limited by the legislature',\n",
    "       'f1.3 Government powers are effectively limited by the judiciary',\n",
    "       'f1.4 Government powers are effectively limited by independent auditing and review',\n",
    "       'f1.5 Government officials are sanctioned for misconduct',\n",
    "       'f1.6 Government powers are subject to non-governmental checks',\n",
    "       'f1.7 Transition of power is subject to the law', 'factor2',\n",
    "       'f2.1 Government officials in the executive branch do not use public office for private gain',\n",
    "       'f2.2 Government officials in the judicial branch do not use public office for private gain',\n",
    "       'f2.3 Government officials in the police and the military do not use public office for private gain',\n",
    "       'f2.4 Government officials in the legislative branch do not use public office for private gain',\n",
    "       'factor3', 'f3.1 Crime is effectively controlled',\n",
    "       'f3.2 Civil conflict is effectively limited',\n",
    "       'f3.3 People do not resort to violence to redress personal grievances',\n",
    "       'factor4', 'f4.1 Equal treatment and absence of discrimination',\n",
    "       'f4.2 The right to life and security of the person is effectively guaranteed',\n",
    "       'f4.3 Due process of law and rights of the accused',\n",
    "       'f4.4 Freedom of opinion and expression is effectively guaranteed',\n",
    "       'f4.5 Freedom of belief and religion is effectively guaranteed',\n",
    "       'f4.6 Freedom from arbitrary interference with privacy is effectively guaranteed',\n",
    "       'f4.7 Freedom of assembly and association is effectively guaranteed',\n",
    "       'f4.8 Fundamental labor rights are effectively guaranteed', 'factor5',\n",
    "       'f5.1 The laws are publicized and accessible',\n",
    "       'f5.2 The laws are stable',\n",
    "       'f5.3 Right to petition the government and public participation',\n",
    "       'f5.4 Official information is available on request', 'factor6',\n",
    "       'f6.1 Government regulations are effectively enforced',\n",
    "       'f6.2 Government regulations are applied and enforced without improper influence',\n",
    "       'f6.3 Administrative proceedings are conducted without unreasonable delay',\n",
    "       'f6.4 Due process is respected in administrative proceedings',\n",
    "       'f6.5 The Government does not expropriate without adequate compensation',\n",
    "       'factor7', 'f7.1 People can access and afford civil justice',\n",
    "       'f7.2 Civil justice is free of discrimination',\n",
    "       'f7.3 Civil justice is free of corruption',\n",
    "       'f7.4 Civil justice is free of improper government influence',\n",
    "       'f7.5 Civil justice is not subject to unreasonable delays',\n",
    "       'f7.6 Civil justice is effectively enforced',\n",
    "       'f7.7 ADRs are accessible, impartial, and effective', 'factor8',\n",
    "       'f8.1 Criminal investigation system is effective',\n",
    "       'f8.2 Criminal adjudication system is timely and effective',\n",
    "       'f8.3 Correctional system is effective in reducing criminal behavior',\n",
    "       'f8.4 Criminal system is impartial',\n",
    "       'f8.5 Criminal system is free of corruption',\n",
    "       'f8.6 Criminal system is free of improper government influence',\n",
    "       'f8.7 Due process of law and rights of the accused', 'gini', 'population']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor1</th>\n",
       "      <th>factor2</th>\n",
       "      <th>factor3</th>\n",
       "      <th>factor4</th>\n",
       "      <th>factor5</th>\n",
       "      <th>factor6</th>\n",
       "      <th>factor7</th>\n",
       "      <th>factor8</th>\n",
       "      <th>Country</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>Region</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.41</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.454301</td>\n",
       "      <td>2.900489e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.419769</td>\n",
       "      <td>4.209522e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Australia</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.347525</td>\n",
       "      <td>2.272825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Austria</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.303947</td>\n",
       "      <td>8.429991e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.38</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>0.431937</td>\n",
       "      <td>1.550000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.431520</td>\n",
       "      <td>9.464000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.275339</td>\n",
       "      <td>1.112825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.28</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.463717</td>\n",
       "      <td>1.023876e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.435052</td>\n",
       "      <td>3.828419e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.632413</td>\n",
       "      <td>2.132822e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.459599</td>\n",
       "      <td>2.020000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.361473</td>\n",
       "      <td>7.305888e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.577919</td>\n",
       "      <td>1.659081e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.40</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.463708</td>\n",
       "      <td>1.483226e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.588098</td>\n",
       "      <td>2.165949e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Canada</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.335666</td>\n",
       "      <td>3.475431e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.491399</td>\n",
       "      <td>1.738844e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>China</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.523397</td>\n",
       "      <td>1.350000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>4.688102e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.37</td>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.586571</td>\n",
       "      <td>2.110264e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.53</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>High income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.321295</td>\n",
       "      <td>4.267558e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>High income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.262622</td>\n",
       "      <td>1.051078e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>5.591572e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.458824</td>\n",
       "      <td>1.015504e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.464752</td>\n",
       "      <td>1.541949e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>0.518462</td>\n",
       "      <td>8.566090e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.25</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.416650</td>\n",
       "      <td>6.072233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>High income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.333455</td>\n",
       "      <td>1.322696e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.555068</td>\n",
       "      <td>9.219121e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>Finland</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.270207</td>\n",
       "      <td>5.413971e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>0.423374</td>\n",
       "      <td>1.770000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.38</td>\n",
       "      <td>Panama</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.519092</td>\n",
       "      <td>3.743761e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.45</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.451560</td>\n",
       "      <td>3.015877e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>9.601732e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Poland</td>\n",
       "      <td>High income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.329352</td>\n",
       "      <td>3.806316e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.363438</td>\n",
       "      <td>1.051484e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.327560</td>\n",
       "      <td>5.000444e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.481151</td>\n",
       "      <td>1.430000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.46</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.580372</td>\n",
       "      <td>1.378011e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.355623</td>\n",
       "      <td>7.199077e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.36</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.560696</td>\n",
       "      <td>6.043157e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.400666</td>\n",
       "      <td>5.312400e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>High income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.255701</td>\n",
       "      <td>2.057159e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.662355</td>\n",
       "      <td>5.234170e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.69</td>\n",
       "      <td>Spain</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.357847</td>\n",
       "      <td>4.677306e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>0.403273</td>\n",
       "      <td>2.032800e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.273059</td>\n",
       "      <td>9.519374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.571853</td>\n",
       "      <td>4.864571e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.515705</td>\n",
       "      <td>6.716413e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>0.564304</td>\n",
       "      <td>1.077750e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.458192</td>\n",
       "      <td>7.409926e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.586754</td>\n",
       "      <td>3.540062e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.39</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.441067</td>\n",
       "      <td>4.559330e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.323718</td>\n",
       "      <td>6.370030e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>United States</td>\n",
       "      <td>High income</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>0.406532</td>\n",
       "      <td>3.140000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.414604</td>\n",
       "      <td>3.396753e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.36</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>0.460922</td>\n",
       "      <td>2.977450e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>0.433430</td>\n",
       "      <td>2.985424e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>0.498665</td>\n",
       "      <td>8.877290e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>1.478658e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    factor1  factor2  factor3  factor4  factor5  factor6  factor7  factor8  \\\n",
       "0      0.46     0.31     0.73     0.63     0.44     0.43     0.51     0.41   \n",
       "1      0.46     0.47     0.60     0.63     0.48     0.43     0.54     0.43   \n",
       "2      0.88     0.90     0.86     0.84     0.84     0.83     0.72     0.72   \n",
       "3      0.82     0.77     0.89     0.82     0.80     0.84     0.74     0.75   \n",
       "4      0.40     0.29     0.62     0.43     0.35     0.36     0.32     0.38   \n",
       "5      0.34     0.50     0.78     0.45     0.36     0.56     0.63     0.59   \n",
       "6      0.78     0.78     0.84     0.81     0.67     0.70     0.68     0.72   \n",
       "7      0.38     0.24     0.67     0.49     0.41     0.37     0.38     0.28   \n",
       "8      0.55     0.47     0.76     0.67     0.49     0.53     0.50     0.62   \n",
       "9      0.73     0.75     0.76     0.59     0.67     0.71     0.65     0.72   \n",
       "10     0.62     0.52     0.64     0.69     0.54     0.56     0.55     0.49   \n",
       "11     0.51     0.46     0.74     0.68     0.53     0.50     0.57     0.39   \n",
       "12     0.43     0.50     0.70     0.59     0.41     0.56     0.59     0.45   \n",
       "13     0.34     0.31     0.70     0.43     0.37     0.33     0.37     0.40   \n",
       "14     0.31     0.20     0.62     0.42     0.27     0.28     0.35     0.32   \n",
       "15     0.78     0.81     0.88     0.78     0.84     0.79     0.72     0.75   \n",
       "16     0.74     0.74     0.70     0.73     0.68     0.66     0.66     0.60   \n",
       "17     0.36     0.52     0.78     0.35     0.42     0.41     0.43     0.54   \n",
       "18     0.55     0.44     0.43     0.55     0.51     0.52     0.53     0.43   \n",
       "19     0.43     0.39     0.58     0.50     0.37     0.48     0.51     0.37   \n",
       "20     0.61     0.55     0.77     0.67     0.53     0.48     0.51     0.53   \n",
       "21     0.71     0.62     0.81     0.79     0.49     0.59     0.65     0.70   \n",
       "22     0.93     0.95     0.91     0.91     0.82     0.85     0.79     0.87   \n",
       "23     0.53     0.36     0.60     0.67     0.52     0.45     0.51     0.47   \n",
       "24     0.41     0.47     0.56     0.56     0.38     0.46     0.42     0.44   \n",
       "25     0.58     0.51     0.67     0.43     0.48     0.42     0.47     0.45   \n",
       "26     0.50     0.45     0.58     0.58     0.37     0.52     0.49     0.25   \n",
       "27     0.79     0.77     0.82     0.79     0.71     0.73     0.71     0.75   \n",
       "28     0.36     0.44     0.56     0.41     0.29     0.36     0.46     0.49   \n",
       "29     0.89     0.93     0.92     0.90     0.84     0.82     0.79     0.87   \n",
       "..      ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "63     0.46     0.28     0.29     0.40     0.35     0.36     0.39     0.39   \n",
       "64     0.45     0.41     0.68     0.63     0.60     0.52     0.51     0.38   \n",
       "65     0.64     0.37     0.62     0.70     0.43     0.48     0.43     0.45   \n",
       "66     0.56     0.41     0.60     0.57     0.46     0.51     0.43     0.42   \n",
       "67     0.78     0.72     0.81     0.85     0.59     0.61     0.63     0.73   \n",
       "68     0.71     0.68     0.74     0.75     0.62     0.57     0.62     0.62   \n",
       "69     0.66     0.74     0.82     0.76     0.74     0.67     0.72     0.76   \n",
       "70     0.31     0.39     0.49     0.47     0.41     0.45     0.50     0.40   \n",
       "71     0.57     0.49     0.65     0.62     0.41     0.58     0.58     0.46   \n",
       "72     0.48     0.42     0.75     0.61     0.44     0.43     0.47     0.45   \n",
       "73     0.55     0.36     0.64     0.63     0.26     0.33     0.54     0.36   \n",
       "74     0.73     0.91     0.93     0.73     0.67     0.80     0.79     0.87   \n",
       "75     0.64     0.62     0.80     0.78     0.63     0.59     0.60     0.59   \n",
       "76     0.62     0.50     0.56     0.64     0.61     0.54     0.55     0.49   \n",
       "77     0.75     0.80     0.79     0.86     0.61     0.67     0.65     0.69   \n",
       "78     0.56     0.51     0.54     0.60     0.50     0.52     0.52     0.62   \n",
       "79     0.92     0.96     0.89     0.93     0.93     0.89     0.78     0.82   \n",
       "80     0.55     0.41     0.61     0.53     0.41     0.44     0.48     0.49   \n",
       "81     0.53     0.41     0.63     0.66     0.50     0.51     0.43     0.59   \n",
       "82     0.58     0.52     0.79     0.56     0.46     0.55     0.56     0.52   \n",
       "83     0.47     0.55     0.63     0.49     0.46     0.55     0.55     0.42   \n",
       "84     0.43     0.32     0.48     0.43     0.36     0.38     0.51     0.43   \n",
       "85     0.36     0.25     0.74     0.58     0.44     0.35     0.52     0.39   \n",
       "86     0.79     0.80     0.84     0.78     0.78     0.79     0.72     0.75   \n",
       "87     0.77     0.78     0.83     0.73     0.77     0.70     0.65     0.65   \n",
       "88     0.70     0.78     0.70     0.75     0.62     0.71     0.71     0.50   \n",
       "89     0.24     0.30     0.89     0.34     0.36     0.46     0.49     0.36   \n",
       "90     0.25     0.32     0.51     0.48     0.36     0.33     0.38     0.24   \n",
       "91     0.40     0.43     0.82     0.48     0.35     0.39     0.43     0.57   \n",
       "92     0.51     0.44     0.67     0.41     0.39     0.41     0.46     0.37   \n",
       "\n",
       "                   Country         Income Group  \\\n",
       "0                  Albania  Lower middle income   \n",
       "1                Argentina  Upper middle income   \n",
       "2                Australia          High income   \n",
       "3                  Austria          High income   \n",
       "4               Bangladesh           Low income   \n",
       "5                  Belarus  Upper middle income   \n",
       "6                  Belgium          High income   \n",
       "7                  Bolivia  Lower middle income   \n",
       "8   Bosnia and Herzegovina  Upper middle income   \n",
       "9                 Botswana  Upper middle income   \n",
       "10                  Brazil  Upper middle income   \n",
       "11                Bulgaria  Upper middle income   \n",
       "12            Burkina Faso           Low income   \n",
       "13                Cambodia           Low income   \n",
       "14                Cameroon  Lower middle income   \n",
       "15                  Canada          High income   \n",
       "16                   Chile  Upper middle income   \n",
       "17                   China  Upper middle income   \n",
       "18                Colombia  Upper middle income   \n",
       "19           Cote d'Ivoire  Lower middle income   \n",
       "20                 Croatia          High income   \n",
       "21          Czech Republic          High income   \n",
       "22                 Denmark          High income   \n",
       "23      Dominican Republic  Upper middle income   \n",
       "24                 Ecuador  Upper middle income   \n",
       "25                   Egypt  Lower middle income   \n",
       "26             El Salvador  Lower middle income   \n",
       "27                 Estonia          High income   \n",
       "28                Ethiopia           Low income   \n",
       "29                 Finland          High income   \n",
       "..                     ...                  ...   \n",
       "63                Pakistan  Lower middle income   \n",
       "64                  Panama  Upper middle income   \n",
       "65                    Peru  Upper middle income   \n",
       "66             Philippines  Lower middle income   \n",
       "67                  Poland          High income   \n",
       "68                Portugal          High income   \n",
       "69       Republic of Korea          High income   \n",
       "70                  Russia  Upper middle income   \n",
       "71                 Senegal  Lower middle income   \n",
       "72                  Serbia  Upper middle income   \n",
       "73            Sierra Leone           Low income   \n",
       "74               Singapore          High income   \n",
       "75                Slovenia          High income   \n",
       "76            South Africa  Upper middle income   \n",
       "77                   Spain          High income   \n",
       "78               Sri Lanka  Lower middle income   \n",
       "79                  Sweden          High income   \n",
       "80                Tanzania           Low income   \n",
       "81                Thailand  Upper middle income   \n",
       "82                 Tunisia  Upper middle income   \n",
       "83                  Turkey  Upper middle income   \n",
       "84                  Uganda           Low income   \n",
       "85                 Ukraine  Lower middle income   \n",
       "86          United Kingdom          High income   \n",
       "87           United States          High income   \n",
       "88                 Uruguay  Upper middle income   \n",
       "89              Uzbekistan  Lower middle income   \n",
       "90               Venezuela  Upper middle income   \n",
       "91                 Vietnam  Lower middle income   \n",
       "92                  Zambia  Lower middle income   \n",
       "\n",
       "                            Region      gini    population  \n",
       "0    Eastern Europe & Central Asia  0.454301  2.900489e+06  \n",
       "1        Latin America & Caribbean  0.419769  4.209522e+07  \n",
       "2              East Asia & Pacific  0.347525  2.272825e+07  \n",
       "3   Western Europe & North America  0.303947  8.429991e+06  \n",
       "4                       South Asia  0.431937  1.550000e+08  \n",
       "5    Eastern Europe & Central Asia  0.431520  9.464000e+06  \n",
       "6   Western Europe & North America  0.275339  1.112825e+07  \n",
       "7        Latin America & Caribbean  0.463717  1.023876e+07  \n",
       "8    Eastern Europe & Central Asia  0.435052  3.828419e+06  \n",
       "9               Sub-Saharan Africa  0.632413  2.132822e+06  \n",
       "10       Latin America & Caribbean  0.459599  2.020000e+08  \n",
       "11   Eastern Europe & Central Asia  0.361473  7.305888e+06  \n",
       "12              Sub-Saharan Africa  0.577919  1.659081e+07  \n",
       "13             East Asia & Pacific  0.463708  1.483226e+07  \n",
       "14              Sub-Saharan Africa  0.588098  2.165949e+07  \n",
       "15  Western Europe & North America  0.335666  3.475431e+07  \n",
       "16       Latin America & Caribbean  0.491399  1.738844e+07  \n",
       "17             East Asia & Pacific  0.523397  1.350000e+09  \n",
       "18       Latin America & Caribbean  0.518447  4.688102e+07  \n",
       "19              Sub-Saharan Africa  0.586571  2.110264e+07  \n",
       "20   Eastern Europe & Central Asia  0.321295  4.267558e+06  \n",
       "21   Eastern Europe & Central Asia  0.262622  1.051078e+07  \n",
       "22  Western Europe & North America  0.289474  5.591572e+06  \n",
       "23       Latin America & Caribbean  0.458824  1.015504e+07  \n",
       "24       Latin America & Caribbean  0.464752  1.541949e+07  \n",
       "25      Middle East & North Africa  0.518462  8.566090e+07  \n",
       "26       Latin America & Caribbean  0.416650  6.072233e+06  \n",
       "27   Eastern Europe & Central Asia  0.333455  1.322696e+06  \n",
       "28              Sub-Saharan Africa  0.555068  9.219121e+07  \n",
       "29  Western Europe & North America  0.270207  5.413971e+06  \n",
       "..                             ...       ...           ...  \n",
       "63                      South Asia  0.423374  1.770000e+08  \n",
       "64       Latin America & Caribbean  0.519092  3.743761e+06  \n",
       "65       Latin America & Caribbean  0.451560  3.015877e+07  \n",
       "66             East Asia & Pacific  0.528220  9.601732e+07  \n",
       "67   Eastern Europe & Central Asia  0.329352  3.806316e+07  \n",
       "68  Western Europe & North America  0.363438  1.051484e+07  \n",
       "69             East Asia & Pacific  0.327560  5.000444e+07  \n",
       "70   Eastern Europe & Central Asia  0.481151  1.430000e+08  \n",
       "71              Sub-Saharan Africa  0.580372  1.378011e+07  \n",
       "72   Eastern Europe & Central Asia  0.355623  7.199077e+06  \n",
       "73              Sub-Saharan Africa  0.560696  6.043157e+06  \n",
       "74             East Asia & Pacific  0.400666  5.312400e+06  \n",
       "75   Eastern Europe & Central Asia  0.255701  2.057159e+06  \n",
       "76              Sub-Saharan Africa  0.662355  5.234170e+07  \n",
       "77  Western Europe & North America  0.357847  4.677306e+07  \n",
       "78                      South Asia  0.403273  2.032800e+07  \n",
       "79  Western Europe & North America  0.273059  9.519374e+06  \n",
       "80              Sub-Saharan Africa  0.571853  4.864571e+07  \n",
       "81             East Asia & Pacific  0.515705  6.716413e+07  \n",
       "82      Middle East & North Africa  0.564304  1.077750e+07  \n",
       "83   Eastern Europe & Central Asia  0.458192  7.409926e+07  \n",
       "84              Sub-Saharan Africa  0.586754  3.540062e+07  \n",
       "85   Eastern Europe & Central Asia  0.441067  4.559330e+07  \n",
       "86  Western Europe & North America  0.323718  6.370030e+07  \n",
       "87  Western Europe & North America  0.406532  3.140000e+08  \n",
       "88       Latin America & Caribbean  0.414604  3.396753e+06  \n",
       "89   Eastern Europe & Central Asia  0.460922  2.977450e+07  \n",
       "90       Latin America & Caribbean  0.433430  2.985424e+07  \n",
       "91             East Asia & Pacific  0.498665  8.877290e+07  \n",
       "92              Sub-Saharan Africa  0.635562  1.478658e+07  \n",
       "\n",
       "[93 rows x 13 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter in a different way\n",
    "c = data[['Country', 'Region','Income Group', 'year', 'isocode', 'factor1', 'factor2','factor3','factor4', 'factor5','factor6','factor7','factor8','gini', 'population']]\n",
    "\n",
    "#All in one line for loops!  \n",
    "#for each cell in this object I have, data, find strings that contain these values\n",
    "#and store that information in factor_frame\n",
    "factor_frame = [x for x in list(data) if x.startswith('factor')]\n",
    "#here's another way to do the same thing\n",
    "factor_frame = [x for x in list(data) if 'factor' in x]\n",
    "\n",
    "# Here's a subset of the data using the list we created from our loop\n",
    "#We want to include a few other columns besides factor by using extend to add items in the list we created\n",
    "factor_frame.extend(['Country', 'Income Group', 'Region', 'gini', 'population'])\n",
    "data[factor_frame]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#rename a column\n",
    "c= c.rename(columns ={'factor1':'govt powers', 'factor2':'corruption', 'factor3':'conflict & violence', 'factor4':'Freedom', 'factor5': 'laws', 'factor6':'regulatory', 'factor7':'criminal justice', 'factor8':'criminal system'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>year</th>\n",
       "      <th>isocode</th>\n",
       "      <th>govt powers</th>\n",
       "      <th>corruption</th>\n",
       "      <th>conflict &amp; violence</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>laws</th>\n",
       "      <th>regulatory</th>\n",
       "      <th>criminal justice</th>\n",
       "      <th>criminal system</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ALB</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.454301</td>\n",
       "      <td>2.900489e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ARG</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.419769</td>\n",
       "      <td>4.209522e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>AUS</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.347525</td>\n",
       "      <td>2.272825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>AUT</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.303947</td>\n",
       "      <td>8.429991e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BGD</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.431937</td>\n",
       "      <td>1.550000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BLR</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.431520</td>\n",
       "      <td>9.464000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BEL</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.275339</td>\n",
       "      <td>1.112825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BOL</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.463717</td>\n",
       "      <td>1.023876e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BIH</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.435052</td>\n",
       "      <td>3.828419e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BWA</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.632413</td>\n",
       "      <td>2.132822e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BRA</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.459599</td>\n",
       "      <td>2.020000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BGR</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.361473</td>\n",
       "      <td>7.305888e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>BFA</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.577919</td>\n",
       "      <td>1.659081e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>KHM</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.463708</td>\n",
       "      <td>1.483226e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>CMR</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.588098</td>\n",
       "      <td>2.165949e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>CAN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.335666</td>\n",
       "      <td>3.475431e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>CHL</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.491399</td>\n",
       "      <td>1.738844e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>China</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>CHN</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.523397</td>\n",
       "      <td>1.350000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>COL</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>4.688102e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>CIV</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.586571</td>\n",
       "      <td>2.110264e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>HRV</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.321295</td>\n",
       "      <td>4.267558e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>CZE</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.262622</td>\n",
       "      <td>1.051078e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>DNK</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>5.591572e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>DOM</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.458824</td>\n",
       "      <td>1.015504e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ECU</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.464752</td>\n",
       "      <td>1.541949e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>EGY</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.518462</td>\n",
       "      <td>8.566090e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SLV</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.416650</td>\n",
       "      <td>6.072233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333455</td>\n",
       "      <td>1.322696e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ETH</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.555068</td>\n",
       "      <td>9.219121e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.270207</td>\n",
       "      <td>5.413971e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>PAK</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.423374</td>\n",
       "      <td>1.770000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Panama</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>PAN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.519092</td>\n",
       "      <td>3.743761e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Peru</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>PER</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.451560</td>\n",
       "      <td>3.015877e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>PHL</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>9.601732e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>POL</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.329352</td>\n",
       "      <td>3.806316e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.363438</td>\n",
       "      <td>1.051484e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>KOR</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.327560</td>\n",
       "      <td>5.000444e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>RUS</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.481151</td>\n",
       "      <td>1.430000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SEN</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.580372</td>\n",
       "      <td>1.378011e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SRB</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.355623</td>\n",
       "      <td>7.199077e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SLE</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.560696</td>\n",
       "      <td>6.043157e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SGP</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.400666</td>\n",
       "      <td>5.312400e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SVN</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.255701</td>\n",
       "      <td>2.057159e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.662355</td>\n",
       "      <td>5.234170e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ESP</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.357847</td>\n",
       "      <td>4.677306e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>LKA</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.403273</td>\n",
       "      <td>2.032800e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>SWE</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.273059</td>\n",
       "      <td>9.519374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>TZA</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.571853</td>\n",
       "      <td>4.864571e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>THA</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.515705</td>\n",
       "      <td>6.716413e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>TUN</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.564304</td>\n",
       "      <td>1.077750e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>TUR</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.458192</td>\n",
       "      <td>7.409926e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>2012</td>\n",
       "      <td>UGA</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.586754</td>\n",
       "      <td>3.540062e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>UKR</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.441067</td>\n",
       "      <td>4.559330e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>GBR</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.323718</td>\n",
       "      <td>6.370030e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>United States</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.406532</td>\n",
       "      <td>3.140000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>URY</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.414604</td>\n",
       "      <td>3.396753e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>Eastern Europe &amp; Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>UZB</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.460922</td>\n",
       "      <td>2.977450e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>VEN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.433430</td>\n",
       "      <td>2.985424e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>VNM</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.498665</td>\n",
       "      <td>8.877290e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>1.478658e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country                          Region  \\\n",
       "0                  Albania   Eastern Europe & Central Asia   \n",
       "1                Argentina       Latin America & Caribbean   \n",
       "2                Australia             East Asia & Pacific   \n",
       "3                  Austria  Western Europe & North America   \n",
       "4               Bangladesh                      South Asia   \n",
       "5                  Belarus   Eastern Europe & Central Asia   \n",
       "6                  Belgium  Western Europe & North America   \n",
       "7                  Bolivia       Latin America & Caribbean   \n",
       "8   Bosnia and Herzegovina   Eastern Europe & Central Asia   \n",
       "9                 Botswana              Sub-Saharan Africa   \n",
       "10                  Brazil       Latin America & Caribbean   \n",
       "11                Bulgaria   Eastern Europe & Central Asia   \n",
       "12            Burkina Faso              Sub-Saharan Africa   \n",
       "13                Cambodia             East Asia & Pacific   \n",
       "14                Cameroon              Sub-Saharan Africa   \n",
       "15                  Canada  Western Europe & North America   \n",
       "16                   Chile       Latin America & Caribbean   \n",
       "17                   China             East Asia & Pacific   \n",
       "18                Colombia       Latin America & Caribbean   \n",
       "19           Cote d'Ivoire              Sub-Saharan Africa   \n",
       "20                 Croatia   Eastern Europe & Central Asia   \n",
       "21          Czech Republic   Eastern Europe & Central Asia   \n",
       "22                 Denmark  Western Europe & North America   \n",
       "23      Dominican Republic       Latin America & Caribbean   \n",
       "24                 Ecuador       Latin America & Caribbean   \n",
       "25                   Egypt      Middle East & North Africa   \n",
       "26             El Salvador       Latin America & Caribbean   \n",
       "27                 Estonia   Eastern Europe & Central Asia   \n",
       "28                Ethiopia              Sub-Saharan Africa   \n",
       "29                 Finland  Western Europe & North America   \n",
       "..                     ...                             ...   \n",
       "63                Pakistan                      South Asia   \n",
       "64                  Panama       Latin America & Caribbean   \n",
       "65                    Peru       Latin America & Caribbean   \n",
       "66             Philippines             East Asia & Pacific   \n",
       "67                  Poland   Eastern Europe & Central Asia   \n",
       "68                Portugal  Western Europe & North America   \n",
       "69       Republic of Korea             East Asia & Pacific   \n",
       "70                  Russia   Eastern Europe & Central Asia   \n",
       "71                 Senegal              Sub-Saharan Africa   \n",
       "72                  Serbia   Eastern Europe & Central Asia   \n",
       "73            Sierra Leone              Sub-Saharan Africa   \n",
       "74               Singapore             East Asia & Pacific   \n",
       "75                Slovenia   Eastern Europe & Central Asia   \n",
       "76            South Africa              Sub-Saharan Africa   \n",
       "77                   Spain  Western Europe & North America   \n",
       "78               Sri Lanka                      South Asia   \n",
       "79                  Sweden  Western Europe & North America   \n",
       "80                Tanzania              Sub-Saharan Africa   \n",
       "81                Thailand             East Asia & Pacific   \n",
       "82                 Tunisia      Middle East & North Africa   \n",
       "83                  Turkey   Eastern Europe & Central Asia   \n",
       "84                  Uganda              Sub-Saharan Africa   \n",
       "85                 Ukraine   Eastern Europe & Central Asia   \n",
       "86          United Kingdom  Western Europe & North America   \n",
       "87           United States  Western Europe & North America   \n",
       "88                 Uruguay       Latin America & Caribbean   \n",
       "89              Uzbekistan   Eastern Europe & Central Asia   \n",
       "90               Venezuela       Latin America & Caribbean   \n",
       "91                 Vietnam             East Asia & Pacific   \n",
       "92                  Zambia              Sub-Saharan Africa   \n",
       "\n",
       "           Income Group  year isocode  govt powers  corruption  \\\n",
       "0   Lower middle income  2012     ALB         0.46        0.31   \n",
       "1   Upper middle income  2012     ARG         0.46        0.47   \n",
       "2           High income  2012     AUS         0.88        0.90   \n",
       "3           High income  2012     AUT         0.82        0.77   \n",
       "4            Low income  2012     BGD         0.40        0.29   \n",
       "5   Upper middle income  2012     BLR         0.34        0.50   \n",
       "6           High income  2012     BEL         0.78        0.78   \n",
       "7   Lower middle income  2012     BOL         0.38        0.24   \n",
       "8   Upper middle income  2012     BIH         0.55        0.47   \n",
       "9   Upper middle income  2012     BWA         0.73        0.75   \n",
       "10  Upper middle income  2012     BRA         0.62        0.52   \n",
       "11  Upper middle income  2012     BGR         0.51        0.46   \n",
       "12           Low income  2012     BFA         0.43        0.50   \n",
       "13           Low income  2012     KHM         0.34        0.31   \n",
       "14  Lower middle income  2012     CMR         0.31        0.20   \n",
       "15          High income  2012     CAN         0.78        0.81   \n",
       "16  Upper middle income  2012     CHL         0.74        0.74   \n",
       "17  Upper middle income  2012     CHN         0.36        0.52   \n",
       "18  Upper middle income  2012     COL         0.55        0.44   \n",
       "19  Lower middle income  2012     CIV         0.43        0.39   \n",
       "20          High income  2012     HRV         0.61        0.55   \n",
       "21          High income  2012     CZE         0.71        0.62   \n",
       "22          High income  2012     DNK         0.93        0.95   \n",
       "23  Upper middle income  2012     DOM         0.53        0.36   \n",
       "24  Upper middle income  2012     ECU         0.41        0.47   \n",
       "25  Lower middle income  2012     EGY         0.58        0.51   \n",
       "26  Lower middle income  2012     SLV         0.50        0.45   \n",
       "27          High income  2012     EST         0.79        0.77   \n",
       "28           Low income  2012     ETH         0.36        0.44   \n",
       "29          High income  2012     FIN         0.89        0.93   \n",
       "..                  ...   ...     ...          ...         ...   \n",
       "63  Lower middle income  2012     PAK         0.46        0.28   \n",
       "64  Upper middle income  2012     PAN         0.45        0.41   \n",
       "65  Upper middle income  2012     PER         0.64        0.37   \n",
       "66  Lower middle income  2012     PHL         0.56        0.41   \n",
       "67          High income  2012     POL         0.78        0.72   \n",
       "68          High income  2012     PRT         0.71        0.68   \n",
       "69          High income  2012     KOR         0.66        0.74   \n",
       "70  Upper middle income  2012     RUS         0.31        0.39   \n",
       "71  Lower middle income  2012     SEN         0.57        0.49   \n",
       "72  Upper middle income  2012     SRB         0.48        0.42   \n",
       "73           Low income  2012     SLE         0.55        0.36   \n",
       "74          High income  2012     SGP         0.73        0.91   \n",
       "75          High income  2012     SVN         0.64        0.62   \n",
       "76  Upper middle income  2012     ZAF         0.62        0.50   \n",
       "77          High income  2012     ESP         0.75        0.80   \n",
       "78  Lower middle income  2012     LKA         0.56        0.51   \n",
       "79          High income  2012     SWE         0.92        0.96   \n",
       "80           Low income  2012     TZA         0.55        0.41   \n",
       "81  Upper middle income  2012     THA         0.53        0.41   \n",
       "82  Upper middle income  2012     TUN         0.58        0.52   \n",
       "83  Upper middle income  2012     TUR         0.47        0.55   \n",
       "84           Low income  2012     UGA         0.43        0.32   \n",
       "85  Lower middle income  2012     UKR         0.36        0.25   \n",
       "86          High income  2012     GBR         0.79        0.80   \n",
       "87          High income  2012     USA         0.77        0.78   \n",
       "88  Upper middle income  2012     URY         0.70        0.78   \n",
       "89  Lower middle income  2012     UZB         0.24        0.30   \n",
       "90  Upper middle income  2012     VEN         0.25        0.32   \n",
       "91  Lower middle income  2012     VNM         0.40        0.43   \n",
       "92  Lower middle income  2012     ZMB         0.51        0.44   \n",
       "\n",
       "    conflict & violence  Freedom  laws  regulatory  criminal justice  \\\n",
       "0                  0.73     0.63  0.44        0.43              0.51   \n",
       "1                  0.60     0.63  0.48        0.43              0.54   \n",
       "2                  0.86     0.84  0.84        0.83              0.72   \n",
       "3                  0.89     0.82  0.80        0.84              0.74   \n",
       "4                  0.62     0.43  0.35        0.36              0.32   \n",
       "5                  0.78     0.45  0.36        0.56              0.63   \n",
       "6                  0.84     0.81  0.67        0.70              0.68   \n",
       "7                  0.67     0.49  0.41        0.37              0.38   \n",
       "8                  0.76     0.67  0.49        0.53              0.50   \n",
       "9                  0.76     0.59  0.67        0.71              0.65   \n",
       "10                 0.64     0.69  0.54        0.56              0.55   \n",
       "11                 0.74     0.68  0.53        0.50              0.57   \n",
       "12                 0.70     0.59  0.41        0.56              0.59   \n",
       "13                 0.70     0.43  0.37        0.33              0.37   \n",
       "14                 0.62     0.42  0.27        0.28              0.35   \n",
       "15                 0.88     0.78  0.84        0.79              0.72   \n",
       "16                 0.70     0.73  0.68        0.66              0.66   \n",
       "17                 0.78     0.35  0.42        0.41              0.43   \n",
       "18                 0.43     0.55  0.51        0.52              0.53   \n",
       "19                 0.58     0.50  0.37        0.48              0.51   \n",
       "20                 0.77     0.67  0.53        0.48              0.51   \n",
       "21                 0.81     0.79  0.49        0.59              0.65   \n",
       "22                 0.91     0.91  0.82        0.85              0.79   \n",
       "23                 0.60     0.67  0.52        0.45              0.51   \n",
       "24                 0.56     0.56  0.38        0.46              0.42   \n",
       "25                 0.67     0.43  0.48        0.42              0.47   \n",
       "26                 0.58     0.58  0.37        0.52              0.49   \n",
       "27                 0.82     0.79  0.71        0.73              0.71   \n",
       "28                 0.56     0.41  0.29        0.36              0.46   \n",
       "29                 0.92     0.90  0.84        0.82              0.79   \n",
       "..                  ...      ...   ...         ...               ...   \n",
       "63                 0.29     0.40  0.35        0.36              0.39   \n",
       "64                 0.68     0.63  0.60        0.52              0.51   \n",
       "65                 0.62     0.70  0.43        0.48              0.43   \n",
       "66                 0.60     0.57  0.46        0.51              0.43   \n",
       "67                 0.81     0.85  0.59        0.61              0.63   \n",
       "68                 0.74     0.75  0.62        0.57              0.62   \n",
       "69                 0.82     0.76  0.74        0.67              0.72   \n",
       "70                 0.49     0.47  0.41        0.45              0.50   \n",
       "71                 0.65     0.62  0.41        0.58              0.58   \n",
       "72                 0.75     0.61  0.44        0.43              0.47   \n",
       "73                 0.64     0.63  0.26        0.33              0.54   \n",
       "74                 0.93     0.73  0.67        0.80              0.79   \n",
       "75                 0.80     0.78  0.63        0.59              0.60   \n",
       "76                 0.56     0.64  0.61        0.54              0.55   \n",
       "77                 0.79     0.86  0.61        0.67              0.65   \n",
       "78                 0.54     0.60  0.50        0.52              0.52   \n",
       "79                 0.89     0.93  0.93        0.89              0.78   \n",
       "80                 0.61     0.53  0.41        0.44              0.48   \n",
       "81                 0.63     0.66  0.50        0.51              0.43   \n",
       "82                 0.79     0.56  0.46        0.55              0.56   \n",
       "83                 0.63     0.49  0.46        0.55              0.55   \n",
       "84                 0.48     0.43  0.36        0.38              0.51   \n",
       "85                 0.74     0.58  0.44        0.35              0.52   \n",
       "86                 0.84     0.78  0.78        0.79              0.72   \n",
       "87                 0.83     0.73  0.77        0.70              0.65   \n",
       "88                 0.70     0.75  0.62        0.71              0.71   \n",
       "89                 0.89     0.34  0.36        0.46              0.49   \n",
       "90                 0.51     0.48  0.36        0.33              0.38   \n",
       "91                 0.82     0.48  0.35        0.39              0.43   \n",
       "92                 0.67     0.41  0.39        0.41              0.46   \n",
       "\n",
       "    criminal system      gini    population  \n",
       "0              0.41  0.454301  2.900489e+06  \n",
       "1              0.43  0.419769  4.209522e+07  \n",
       "2              0.72  0.347525  2.272825e+07  \n",
       "3              0.75  0.303947  8.429991e+06  \n",
       "4              0.38  0.431937  1.550000e+08  \n",
       "5              0.59  0.431520  9.464000e+06  \n",
       "6              0.72  0.275339  1.112825e+07  \n",
       "7              0.28  0.463717  1.023876e+07  \n",
       "8              0.62  0.435052  3.828419e+06  \n",
       "9              0.72  0.632413  2.132822e+06  \n",
       "10             0.49  0.459599  2.020000e+08  \n",
       "11             0.39  0.361473  7.305888e+06  \n",
       "12             0.45  0.577919  1.659081e+07  \n",
       "13             0.40  0.463708  1.483226e+07  \n",
       "14             0.32  0.588098  2.165949e+07  \n",
       "15             0.75  0.335666  3.475431e+07  \n",
       "16             0.60  0.491399  1.738844e+07  \n",
       "17             0.54  0.523397  1.350000e+09  \n",
       "18             0.43  0.518447  4.688102e+07  \n",
       "19             0.37  0.586571  2.110264e+07  \n",
       "20             0.53  0.321295  4.267558e+06  \n",
       "21             0.70  0.262622  1.051078e+07  \n",
       "22             0.87  0.289474  5.591572e+06  \n",
       "23             0.47  0.458824  1.015504e+07  \n",
       "24             0.44  0.464752  1.541949e+07  \n",
       "25             0.45  0.518462  8.566090e+07  \n",
       "26             0.25  0.416650  6.072233e+06  \n",
       "27             0.75  0.333455  1.322696e+06  \n",
       "28             0.49  0.555068  9.219121e+07  \n",
       "29             0.87  0.270207  5.413971e+06  \n",
       "..              ...       ...           ...  \n",
       "63             0.39  0.423374  1.770000e+08  \n",
       "64             0.38  0.519092  3.743761e+06  \n",
       "65             0.45  0.451560  3.015877e+07  \n",
       "66             0.42  0.528220  9.601732e+07  \n",
       "67             0.73  0.329352  3.806316e+07  \n",
       "68             0.62  0.363438  1.051484e+07  \n",
       "69             0.76  0.327560  5.000444e+07  \n",
       "70             0.40  0.481151  1.430000e+08  \n",
       "71             0.46  0.580372  1.378011e+07  \n",
       "72             0.45  0.355623  7.199077e+06  \n",
       "73             0.36  0.560696  6.043157e+06  \n",
       "74             0.87  0.400666  5.312400e+06  \n",
       "75             0.59  0.255701  2.057159e+06  \n",
       "76             0.49  0.662355  5.234170e+07  \n",
       "77             0.69  0.357847  4.677306e+07  \n",
       "78             0.62  0.403273  2.032800e+07  \n",
       "79             0.82  0.273059  9.519374e+06  \n",
       "80             0.49  0.571853  4.864571e+07  \n",
       "81             0.59  0.515705  6.716413e+07  \n",
       "82             0.52  0.564304  1.077750e+07  \n",
       "83             0.42  0.458192  7.409926e+07  \n",
       "84             0.43  0.586754  3.540062e+07  \n",
       "85             0.39  0.441067  4.559330e+07  \n",
       "86             0.75  0.323718  6.370030e+07  \n",
       "87             0.65  0.406532  3.140000e+08  \n",
       "88             0.50  0.414604  3.396753e+06  \n",
       "89             0.36  0.460922  2.977450e+07  \n",
       "90             0.24  0.433430  2.985424e+07  \n",
       "91             0.57  0.498665  8.877290e+07  \n",
       "92             0.37  0.635562  1.478658e+07  \n",
       "\n",
       "[93 rows x 15 columns]"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter for a particular year\n",
    "c[c['year']==2012]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>year</th>\n",
       "      <th>isocode</th>\n",
       "      <th>govt powers</th>\n",
       "      <th>corruption</th>\n",
       "      <th>conflict &amp; violence</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>laws</th>\n",
       "      <th>regulatory</th>\n",
       "      <th>criminal justice</th>\n",
       "      <th>criminal system</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Greece</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>GRC</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.364873</td>\n",
       "      <td>11092771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.350970</td>\n",
       "      <td>59539717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.363438</td>\n",
       "      <td>10514844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>ESP</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.357847</td>\n",
       "      <td>46773055.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>United States</td>\n",
       "      <td>Western Europe &amp; North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2012</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.406532</td>\n",
       "      <td>314000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country                          Region Income Group  year isocode  \\\n",
       "34         Greece  Western Europe & North America  High income  2012     GRC   \n",
       "41          Italy  Western Europe & North America  High income  2012     ITA   \n",
       "68       Portugal  Western Europe & North America  High income  2012     PRT   \n",
       "77          Spain  Western Europe & North America  High income  2012     ESP   \n",
       "87  United States  Western Europe & North America  High income  2012     USA   \n",
       "\n",
       "    govt powers  corruption  conflict & violence  Freedom  laws  regulatory  \\\n",
       "34         0.64        0.56                 0.73     0.72  0.51        0.54   \n",
       "41         0.67        0.62                 0.76     0.72  0.49        0.56   \n",
       "68         0.71        0.68                 0.74     0.75  0.62        0.57   \n",
       "77         0.75        0.80                 0.79     0.86  0.61        0.67   \n",
       "87         0.77        0.78                 0.83     0.73  0.77        0.70   \n",
       "\n",
       "    criminal justice  criminal system      gini   population  \n",
       "34              0.61             0.50  0.364873   11092771.0  \n",
       "41              0.56             0.67  0.350970   59539717.0  \n",
       "68              0.62             0.62  0.363438   10514844.0  \n",
       "77              0.65             0.69  0.357847   46773055.0  \n",
       "87              0.65             0.65  0.406532  314000000.0  "
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or 2 categories\n",
    "c[(c['Region'] == 'Western Europe & North America') & (c['gini'] >= .35)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Countries in ds: 93\n",
      "Number of years in ds: 1\n"
     ]
    }
   ],
   "source": [
    "#How many countries? (temporarily store this value for future use)\n",
    "nc = c['isocode'].nunique()\n",
    "#How many observations? \n",
    "print('Number of Countries in ds:', len(c)) #print in the output with string and some information you just calculated in your output box\n",
    "#How many years? \n",
    "print('Number of years in ds:', c['year'].nunique())\n",
    "#drop a column that is unneeded\n",
    "c = c.drop(['year'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#What is the averageinequality by region?\n",
    "reg = c[['gini','Region']].groupby('Region').mean().reset_index()\n",
    "#sort values\n",
    "reg = reg.sort_values('gini', ascending= False)\n",
    "#export this new table (or any data!)\n",
    "reg.to_csv('inequalitytable.csv') #this will save in your workign directory, but you can also suggest exact locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>govt powers</th>\n",
       "      <th>corruption</th>\n",
       "      <th>conflict &amp; violence</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>laws</th>\n",
       "      <th>regulatory</th>\n",
       "      <th>criminal justice</th>\n",
       "      <th>criminal system</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>0.773571</td>\n",
       "      <td>0.790357</td>\n",
       "      <td>0.842500</td>\n",
       "      <td>0.797143</td>\n",
       "      <td>0.717857</td>\n",
       "      <td>0.721786</td>\n",
       "      <td>0.697500</td>\n",
       "      <td>0.724286</td>\n",
       "      <td>0.331954</td>\n",
       "      <td>3.686233e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low income</th>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>0.646429</td>\n",
       "      <td>0.527143</td>\n",
       "      <td>0.398571</td>\n",
       "      <td>0.405714</td>\n",
       "      <td>0.477857</td>\n",
       "      <td>0.430714</td>\n",
       "      <td>0.533374</td>\n",
       "      <td>3.657738e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>0.470870</td>\n",
       "      <td>0.373913</td>\n",
       "      <td>0.649565</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.446087</td>\n",
       "      <td>0.475217</td>\n",
       "      <td>0.416087</td>\n",
       "      <td>0.497740</td>\n",
       "      <td>1.032511e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle income</th>\n",
       "      <td>0.512143</td>\n",
       "      <td>0.497500</td>\n",
       "      <td>0.663929</td>\n",
       "      <td>0.575714</td>\n",
       "      <td>0.489643</td>\n",
       "      <td>0.519643</td>\n",
       "      <td>0.530357</td>\n",
       "      <td>0.482143</td>\n",
       "      <td>0.485861</td>\n",
       "      <td>8.512396e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     govt powers  corruption  conflict & violence   Freedom  \\\n",
       "Income Group                                                                  \n",
       "High income             0.773571    0.790357             0.842500  0.797143   \n",
       "Low income              0.475000    0.371429             0.646429  0.527143   \n",
       "Lower middle income     0.470870    0.373913             0.649565  0.521739   \n",
       "Upper middle income     0.512143    0.497500             0.663929  0.575714   \n",
       "\n",
       "                         laws  regulatory  criminal justice  criminal system  \\\n",
       "Income Group                                                                   \n",
       "High income          0.717857    0.721786          0.697500         0.724286   \n",
       "Low income           0.398571    0.405714          0.477857         0.430714   \n",
       "Lower middle income  0.420000    0.446087          0.475217         0.416087   \n",
       "Upper middle income  0.489643    0.519643          0.530357         0.482143   \n",
       "\n",
       "                         gini    population  \n",
       "Income Group                                 \n",
       "High income          0.331954  3.686233e+07  \n",
       "Low income           0.533374  3.657738e+07  \n",
       "Lower middle income  0.497740  1.032511e+08  \n",
       "Upper middle income  0.485861  8.512396e+07  "
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will filter a partiular subset of the data and then group that data by region and income to find the mean gini\n",
    "reg = data[['gini','Region','Income Group']].groupby(['Region', 'Income Group']).mean()\n",
    "# we can also do this for the entire dataframe\n",
    "c.groupby('Income Group').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#add columns together (or create any function)\n",
    "c['total'] = c['govt powers'] + c['corruption']+c['conflict & violence']+c['Freedom']+c['laws']+c['regulatory']+c['criminal justice']+c['criminal system']\n",
    "c['avg'] = c['total']/8 \n",
    "c['total'] = c['total'].astype(float) #change the column called total within the dataframe c to be stored in float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x142ac5eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I'm using the matplotlib library that we loaded earlier to fo the histogram\n",
    "graph = c['avg'].hist()\n",
    "#save the figure in pdf or jpeg etc.\n",
    "plt.savefig('histofavgini.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>isocode</th>\n",
       "      <th>govt powers</th>\n",
       "      <th>corruption</th>\n",
       "      <th>conflict &amp; violence</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>laws</th>\n",
       "      <th>regulatory</th>\n",
       "      <th>criminal justice</th>\n",
       "      <th>criminal system</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>total</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>ALB</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.454301</td>\n",
       "      <td>2.900489e+06</td>\n",
       "      <td>3.92</td>\n",
       "      <td>0.49000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>ARG</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.419769</td>\n",
       "      <td>4.209522e+07</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.50500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>AUS</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.347525</td>\n",
       "      <td>2.272825e+07</td>\n",
       "      <td>6.59</td>\n",
       "      <td>0.82375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>AUT</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.303947</td>\n",
       "      <td>8.429991e+06</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.80375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>BGD</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.431937</td>\n",
       "      <td>1.550000e+08</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.39375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BLR</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.431520</td>\n",
       "      <td>9.464000e+06</td>\n",
       "      <td>4.21</td>\n",
       "      <td>0.52625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>BEL</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.275339</td>\n",
       "      <td>1.112825e+07</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.74750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>BOL</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.463717</td>\n",
       "      <td>1.023876e+07</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.40250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BIH</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.435052</td>\n",
       "      <td>3.828419e+06</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.57375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BWA</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.632413</td>\n",
       "      <td>2.132822e+06</td>\n",
       "      <td>5.58</td>\n",
       "      <td>0.69750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BRA</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.459599</td>\n",
       "      <td>2.020000e+08</td>\n",
       "      <td>4.61</td>\n",
       "      <td>0.57625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BGR</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.361473</td>\n",
       "      <td>7.305888e+06</td>\n",
       "      <td>4.38</td>\n",
       "      <td>0.54750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>BFA</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.577919</td>\n",
       "      <td>1.659081e+07</td>\n",
       "      <td>4.23</td>\n",
       "      <td>0.52875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Low income</td>\n",
       "      <td>KHM</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.463708</td>\n",
       "      <td>1.483226e+07</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.40625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>CMR</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.588098</td>\n",
       "      <td>2.165949e+07</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.34625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>CAN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.335666</td>\n",
       "      <td>3.475431e+07</td>\n",
       "      <td>6.35</td>\n",
       "      <td>0.79375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>CHL</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.491399</td>\n",
       "      <td>1.738844e+07</td>\n",
       "      <td>5.51</td>\n",
       "      <td>0.68875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>China</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>CHN</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.523397</td>\n",
       "      <td>1.350000e+09</td>\n",
       "      <td>3.81</td>\n",
       "      <td>0.47625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>COL</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>4.688102e+07</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.49500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>CIV</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.586571</td>\n",
       "      <td>2.110264e+07</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.45375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>HRV</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.321295</td>\n",
       "      <td>4.267558e+06</td>\n",
       "      <td>4.65</td>\n",
       "      <td>0.58125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>CZE</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.262622</td>\n",
       "      <td>1.051078e+07</td>\n",
       "      <td>5.36</td>\n",
       "      <td>0.67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>DNK</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>5.591572e+06</td>\n",
       "      <td>7.03</td>\n",
       "      <td>0.87875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>DOM</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.458824</td>\n",
       "      <td>1.015504e+07</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.51375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>ECU</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.464752</td>\n",
       "      <td>1.541949e+07</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.46250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>EGY</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.518462</td>\n",
       "      <td>8.566090e+07</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.50125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>SLV</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.416650</td>\n",
       "      <td>6.072233e+06</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0.46750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333455</td>\n",
       "      <td>1.322696e+06</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0.75875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>ETH</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.555068</td>\n",
       "      <td>9.219121e+07</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.42125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.270207</td>\n",
       "      <td>5.413971e+06</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.87000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>PAK</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.423374</td>\n",
       "      <td>1.770000e+08</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.36500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Panama</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>PAN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.519092</td>\n",
       "      <td>3.743761e+06</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.52250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Peru</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>PER</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.451560</td>\n",
       "      <td>3.015877e+07</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>PHL</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>9.601732e+07</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.49500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>POL</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.329352</td>\n",
       "      <td>3.806316e+07</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.71500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.363438</td>\n",
       "      <td>1.051484e+07</td>\n",
       "      <td>5.31</td>\n",
       "      <td>0.66375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>KOR</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.327560</td>\n",
       "      <td>5.000444e+07</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.73375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>RUS</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.481151</td>\n",
       "      <td>1.430000e+08</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.42750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>SEN</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.580372</td>\n",
       "      <td>1.378011e+07</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.54500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>SRB</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.355623</td>\n",
       "      <td>7.199077e+06</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.50625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>SLE</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.560696</td>\n",
       "      <td>6.043157e+06</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.45875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>SGP</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.400666</td>\n",
       "      <td>5.312400e+06</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.80375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>SVN</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.255701</td>\n",
       "      <td>2.057159e+06</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0.65625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.662355</td>\n",
       "      <td>5.234170e+07</td>\n",
       "      <td>4.51</td>\n",
       "      <td>0.56375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>ESP</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.357847</td>\n",
       "      <td>4.677306e+07</td>\n",
       "      <td>5.82</td>\n",
       "      <td>0.72750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>LKA</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.403273</td>\n",
       "      <td>2.032800e+07</td>\n",
       "      <td>4.37</td>\n",
       "      <td>0.54625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>SWE</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.273059</td>\n",
       "      <td>9.519374e+06</td>\n",
       "      <td>7.12</td>\n",
       "      <td>0.89000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>TZA</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.571853</td>\n",
       "      <td>4.864571e+07</td>\n",
       "      <td>3.92</td>\n",
       "      <td>0.49000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>THA</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.515705</td>\n",
       "      <td>6.716413e+07</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.53250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>TUN</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.564304</td>\n",
       "      <td>1.077750e+07</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.56750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>TUR</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.458192</td>\n",
       "      <td>7.409926e+07</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>UGA</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.586754</td>\n",
       "      <td>3.540062e+07</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.41750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>UKR</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.441067</td>\n",
       "      <td>4.559330e+07</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.45375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>GBR</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.323718</td>\n",
       "      <td>6.370030e+07</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>United States</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.406532</td>\n",
       "      <td>3.140000e+08</td>\n",
       "      <td>5.88</td>\n",
       "      <td>0.73500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>URY</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.414604</td>\n",
       "      <td>3.396753e+06</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.68375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>UZB</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.460922</td>\n",
       "      <td>2.977450e+07</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>VEN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.433430</td>\n",
       "      <td>2.985424e+07</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.35875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>VNM</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.498665</td>\n",
       "      <td>8.877290e+07</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.48375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>1.478658e+07</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.45750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country                         Region  \\\n",
       "0                  Albania   Eastern Europe  Central Asia   \n",
       "1                Argentina       Latin America  Caribbean   \n",
       "2                Australia             East Asia  Pacific   \n",
       "3                  Austria  Western Europe  North America   \n",
       "4               Bangladesh                     South Asia   \n",
       "5                  Belarus   Eastern Europe  Central Asia   \n",
       "6                  Belgium  Western Europe  North America   \n",
       "7                  Bolivia       Latin America  Caribbean   \n",
       "8   Bosnia and Herzegovina   Eastern Europe  Central Asia   \n",
       "9                 Botswana              SubSaharan Africa   \n",
       "10                  Brazil       Latin America  Caribbean   \n",
       "11                Bulgaria   Eastern Europe  Central Asia   \n",
       "12            Burkina Faso              SubSaharan Africa   \n",
       "13                Cambodia             East Asia  Pacific   \n",
       "14                Cameroon              SubSaharan Africa   \n",
       "15                  Canada  Western Europe  North America   \n",
       "16                   Chile       Latin America  Caribbean   \n",
       "17                   China             East Asia  Pacific   \n",
       "18                Colombia       Latin America  Caribbean   \n",
       "19           Cote d'Ivoire              SubSaharan Africa   \n",
       "20                 Croatia   Eastern Europe  Central Asia   \n",
       "21          Czech Republic   Eastern Europe  Central Asia   \n",
       "22                 Denmark  Western Europe  North America   \n",
       "23      Dominican Republic       Latin America  Caribbean   \n",
       "24                 Ecuador       Latin America  Caribbean   \n",
       "25                   Egypt      Middle East  North Africa   \n",
       "26             El Salvador       Latin America  Caribbean   \n",
       "27                 Estonia   Eastern Europe  Central Asia   \n",
       "28                Ethiopia              SubSaharan Africa   \n",
       "29                 Finland  Western Europe  North America   \n",
       "..                     ...                            ...   \n",
       "63                Pakistan                     South Asia   \n",
       "64                  Panama       Latin America  Caribbean   \n",
       "65                    Peru       Latin America  Caribbean   \n",
       "66             Philippines             East Asia  Pacific   \n",
       "67                  Poland   Eastern Europe  Central Asia   \n",
       "68                Portugal  Western Europe  North America   \n",
       "69       Republic of Korea             East Asia  Pacific   \n",
       "70                  Russia   Eastern Europe  Central Asia   \n",
       "71                 Senegal              SubSaharan Africa   \n",
       "72                  Serbia   Eastern Europe  Central Asia   \n",
       "73            Sierra Leone              SubSaharan Africa   \n",
       "74               Singapore             East Asia  Pacific   \n",
       "75                Slovenia   Eastern Europe  Central Asia   \n",
       "76            South Africa              SubSaharan Africa   \n",
       "77                   Spain  Western Europe  North America   \n",
       "78               Sri Lanka                     South Asia   \n",
       "79                  Sweden  Western Europe  North America   \n",
       "80                Tanzania              SubSaharan Africa   \n",
       "81                Thailand             East Asia  Pacific   \n",
       "82                 Tunisia      Middle East  North Africa   \n",
       "83                  Turkey   Eastern Europe  Central Asia   \n",
       "84                  Uganda              SubSaharan Africa   \n",
       "85                 Ukraine   Eastern Europe  Central Asia   \n",
       "86          United Kingdom  Western Europe  North America   \n",
       "87           United States  Western Europe  North America   \n",
       "88                 Uruguay       Latin America  Caribbean   \n",
       "89              Uzbekistan   Eastern Europe  Central Asia   \n",
       "90               Venezuela       Latin America  Caribbean   \n",
       "91                 Vietnam             East Asia  Pacific   \n",
       "92                  Zambia              SubSaharan Africa   \n",
       "\n",
       "           Income Group isocode  govt powers  corruption  conflict & violence  \\\n",
       "0   Lower middle income     ALB         0.46        0.31                 0.73   \n",
       "1   Upper middle income     ARG         0.46        0.47                 0.60   \n",
       "2           High income     AUS         0.88        0.90                 0.86   \n",
       "3           High income     AUT         0.82        0.77                 0.89   \n",
       "4            Low income     BGD         0.40        0.29                 0.62   \n",
       "5   Upper middle income     BLR         0.34        0.50                 0.78   \n",
       "6           High income     BEL         0.78        0.78                 0.84   \n",
       "7   Lower middle income     BOL         0.38        0.24                 0.67   \n",
       "8   Upper middle income     BIH         0.55        0.47                 0.76   \n",
       "9   Upper middle income     BWA         0.73        0.75                 0.76   \n",
       "10  Upper middle income     BRA         0.62        0.52                 0.64   \n",
       "11  Upper middle income     BGR         0.51        0.46                 0.74   \n",
       "12           Low income     BFA         0.43        0.50                 0.70   \n",
       "13           Low income     KHM         0.34        0.31                 0.70   \n",
       "14  Lower middle income     CMR         0.31        0.20                 0.62   \n",
       "15          High income     CAN         0.78        0.81                 0.88   \n",
       "16  Upper middle income     CHL         0.74        0.74                 0.70   \n",
       "17  Upper middle income     CHN         0.36        0.52                 0.78   \n",
       "18  Upper middle income     COL         0.55        0.44                 0.43   \n",
       "19  Lower middle income     CIV         0.43        0.39                 0.58   \n",
       "20          High income     HRV         0.61        0.55                 0.77   \n",
       "21          High income     CZE         0.71        0.62                 0.81   \n",
       "22          High income     DNK         0.93        0.95                 0.91   \n",
       "23  Upper middle income     DOM         0.53        0.36                 0.60   \n",
       "24  Upper middle income     ECU         0.41        0.47                 0.56   \n",
       "25  Lower middle income     EGY         0.58        0.51                 0.67   \n",
       "26  Lower middle income     SLV         0.50        0.45                 0.58   \n",
       "27          High income     EST         0.79        0.77                 0.82   \n",
       "28           Low income     ETH         0.36        0.44                 0.56   \n",
       "29          High income     FIN         0.89        0.93                 0.92   \n",
       "..                  ...     ...          ...         ...                  ...   \n",
       "63  Lower middle income     PAK         0.46        0.28                 0.29   \n",
       "64  Upper middle income     PAN         0.45        0.41                 0.68   \n",
       "65  Upper middle income     PER         0.64        0.37                 0.62   \n",
       "66  Lower middle income     PHL         0.56        0.41                 0.60   \n",
       "67          High income     POL         0.78        0.72                 0.81   \n",
       "68          High income     PRT         0.71        0.68                 0.74   \n",
       "69          High income     KOR         0.66        0.74                 0.82   \n",
       "70  Upper middle income     RUS         0.31        0.39                 0.49   \n",
       "71  Lower middle income     SEN         0.57        0.49                 0.65   \n",
       "72  Upper middle income     SRB         0.48        0.42                 0.75   \n",
       "73           Low income     SLE         0.55        0.36                 0.64   \n",
       "74          High income     SGP         0.73        0.91                 0.93   \n",
       "75          High income     SVN         0.64        0.62                 0.80   \n",
       "76  Upper middle income     ZAF         0.62        0.50                 0.56   \n",
       "77          High income     ESP         0.75        0.80                 0.79   \n",
       "78  Lower middle income     LKA         0.56        0.51                 0.54   \n",
       "79          High income     SWE         0.92        0.96                 0.89   \n",
       "80           Low income     TZA         0.55        0.41                 0.61   \n",
       "81  Upper middle income     THA         0.53        0.41                 0.63   \n",
       "82  Upper middle income     TUN         0.58        0.52                 0.79   \n",
       "83  Upper middle income     TUR         0.47        0.55                 0.63   \n",
       "84           Low income     UGA         0.43        0.32                 0.48   \n",
       "85  Lower middle income     UKR         0.36        0.25                 0.74   \n",
       "86          High income     GBR         0.79        0.80                 0.84   \n",
       "87          High income     USA         0.77        0.78                 0.83   \n",
       "88  Upper middle income     URY         0.70        0.78                 0.70   \n",
       "89  Lower middle income     UZB         0.24        0.30                 0.89   \n",
       "90  Upper middle income     VEN         0.25        0.32                 0.51   \n",
       "91  Lower middle income     VNM         0.40        0.43                 0.82   \n",
       "92  Lower middle income     ZMB         0.51        0.44                 0.67   \n",
       "\n",
       "    Freedom  laws  regulatory  criminal justice  criminal system      gini  \\\n",
       "0      0.63  0.44        0.43              0.51             0.41  0.454301   \n",
       "1      0.63  0.48        0.43              0.54             0.43  0.419769   \n",
       "2      0.84  0.84        0.83              0.72             0.72  0.347525   \n",
       "3      0.82  0.80        0.84              0.74             0.75  0.303947   \n",
       "4      0.43  0.35        0.36              0.32             0.38  0.431937   \n",
       "5      0.45  0.36        0.56              0.63             0.59  0.431520   \n",
       "6      0.81  0.67        0.70              0.68             0.72  0.275339   \n",
       "7      0.49  0.41        0.37              0.38             0.28  0.463717   \n",
       "8      0.67  0.49        0.53              0.50             0.62  0.435052   \n",
       "9      0.59  0.67        0.71              0.65             0.72  0.632413   \n",
       "10     0.69  0.54        0.56              0.55             0.49  0.459599   \n",
       "11     0.68  0.53        0.50              0.57             0.39  0.361473   \n",
       "12     0.59  0.41        0.56              0.59             0.45  0.577919   \n",
       "13     0.43  0.37        0.33              0.37             0.40  0.463708   \n",
       "14     0.42  0.27        0.28              0.35             0.32  0.588098   \n",
       "15     0.78  0.84        0.79              0.72             0.75  0.335666   \n",
       "16     0.73  0.68        0.66              0.66             0.60  0.491399   \n",
       "17     0.35  0.42        0.41              0.43             0.54  0.523397   \n",
       "18     0.55  0.51        0.52              0.53             0.43  0.518447   \n",
       "19     0.50  0.37        0.48              0.51             0.37  0.586571   \n",
       "20     0.67  0.53        0.48              0.51             0.53  0.321295   \n",
       "21     0.79  0.49        0.59              0.65             0.70  0.262622   \n",
       "22     0.91  0.82        0.85              0.79             0.87  0.289474   \n",
       "23     0.67  0.52        0.45              0.51             0.47  0.458824   \n",
       "24     0.56  0.38        0.46              0.42             0.44  0.464752   \n",
       "25     0.43  0.48        0.42              0.47             0.45  0.518462   \n",
       "26     0.58  0.37        0.52              0.49             0.25  0.416650   \n",
       "27     0.79  0.71        0.73              0.71             0.75  0.333455   \n",
       "28     0.41  0.29        0.36              0.46             0.49  0.555068   \n",
       "29     0.90  0.84        0.82              0.79             0.87  0.270207   \n",
       "..      ...   ...         ...               ...              ...       ...   \n",
       "63     0.40  0.35        0.36              0.39             0.39  0.423374   \n",
       "64     0.63  0.60        0.52              0.51             0.38  0.519092   \n",
       "65     0.70  0.43        0.48              0.43             0.45  0.451560   \n",
       "66     0.57  0.46        0.51              0.43             0.42  0.528220   \n",
       "67     0.85  0.59        0.61              0.63             0.73  0.329352   \n",
       "68     0.75  0.62        0.57              0.62             0.62  0.363438   \n",
       "69     0.76  0.74        0.67              0.72             0.76  0.327560   \n",
       "70     0.47  0.41        0.45              0.50             0.40  0.481151   \n",
       "71     0.62  0.41        0.58              0.58             0.46  0.580372   \n",
       "72     0.61  0.44        0.43              0.47             0.45  0.355623   \n",
       "73     0.63  0.26        0.33              0.54             0.36  0.560696   \n",
       "74     0.73  0.67        0.80              0.79             0.87  0.400666   \n",
       "75     0.78  0.63        0.59              0.60             0.59  0.255701   \n",
       "76     0.64  0.61        0.54              0.55             0.49  0.662355   \n",
       "77     0.86  0.61        0.67              0.65             0.69  0.357847   \n",
       "78     0.60  0.50        0.52              0.52             0.62  0.403273   \n",
       "79     0.93  0.93        0.89              0.78             0.82  0.273059   \n",
       "80     0.53  0.41        0.44              0.48             0.49  0.571853   \n",
       "81     0.66  0.50        0.51              0.43             0.59  0.515705   \n",
       "82     0.56  0.46        0.55              0.56             0.52  0.564304   \n",
       "83     0.49  0.46        0.55              0.55             0.42  0.458192   \n",
       "84     0.43  0.36        0.38              0.51             0.43  0.586754   \n",
       "85     0.58  0.44        0.35              0.52             0.39  0.441067   \n",
       "86     0.78  0.78        0.79              0.72             0.75  0.323718   \n",
       "87     0.73  0.77        0.70              0.65             0.65  0.406532   \n",
       "88     0.75  0.62        0.71              0.71             0.50  0.414604   \n",
       "89     0.34  0.36        0.46              0.49             0.36  0.460922   \n",
       "90     0.48  0.36        0.33              0.38             0.24  0.433430   \n",
       "91     0.48  0.35        0.39              0.43             0.57  0.498665   \n",
       "92     0.41  0.39        0.41              0.46             0.37  0.635562   \n",
       "\n",
       "      population  total      avg  \n",
       "0   2.900489e+06   3.92  0.49000  \n",
       "1   4.209522e+07   4.04  0.50500  \n",
       "2   2.272825e+07   6.59  0.82375  \n",
       "3   8.429991e+06   6.43  0.80375  \n",
       "4   1.550000e+08   3.15  0.39375  \n",
       "5   9.464000e+06   4.21  0.52625  \n",
       "6   1.112825e+07   5.98  0.74750  \n",
       "7   1.023876e+07   3.22  0.40250  \n",
       "8   3.828419e+06   4.59  0.57375  \n",
       "9   2.132822e+06   5.58  0.69750  \n",
       "10  2.020000e+08   4.61  0.57625  \n",
       "11  7.305888e+06   4.38  0.54750  \n",
       "12  1.659081e+07   4.23  0.52875  \n",
       "13  1.483226e+07   3.25  0.40625  \n",
       "14  2.165949e+07   2.77  0.34625  \n",
       "15  3.475431e+07   6.35  0.79375  \n",
       "16  1.738844e+07   5.51  0.68875  \n",
       "17  1.350000e+09   3.81  0.47625  \n",
       "18  4.688102e+07   3.96  0.49500  \n",
       "19  2.110264e+07   3.63  0.45375  \n",
       "20  4.267558e+06   4.65  0.58125  \n",
       "21  1.051078e+07   5.36  0.67000  \n",
       "22  5.591572e+06   7.03  0.87875  \n",
       "23  1.015504e+07   4.11  0.51375  \n",
       "24  1.541949e+07   3.70  0.46250  \n",
       "25  8.566090e+07   4.01  0.50125  \n",
       "26  6.072233e+06   3.74  0.46750  \n",
       "27  1.322696e+06   6.07  0.75875  \n",
       "28  9.219121e+07   3.37  0.42125  \n",
       "29  5.413971e+06   6.96  0.87000  \n",
       "..           ...    ...      ...  \n",
       "63  1.770000e+08   2.92  0.36500  \n",
       "64  3.743761e+06   4.18  0.52250  \n",
       "65  3.015877e+07   4.12  0.51500  \n",
       "66  9.601732e+07   3.96  0.49500  \n",
       "67  3.806316e+07   5.72  0.71500  \n",
       "68  1.051484e+07   5.31  0.66375  \n",
       "69  5.000444e+07   5.87  0.73375  \n",
       "70  1.430000e+08   3.42  0.42750  \n",
       "71  1.378011e+07   4.36  0.54500  \n",
       "72  7.199077e+06   4.05  0.50625  \n",
       "73  6.043157e+06   3.67  0.45875  \n",
       "74  5.312400e+06   6.43  0.80375  \n",
       "75  2.057159e+06   5.25  0.65625  \n",
       "76  5.234170e+07   4.51  0.56375  \n",
       "77  4.677306e+07   5.82  0.72750  \n",
       "78  2.032800e+07   4.37  0.54625  \n",
       "79  9.519374e+06   7.12  0.89000  \n",
       "80  4.864571e+07   3.92  0.49000  \n",
       "81  6.716413e+07   4.26  0.53250  \n",
       "82  1.077750e+07   4.54  0.56750  \n",
       "83  7.409926e+07   4.12  0.51500  \n",
       "84  3.540062e+07   3.34  0.41750  \n",
       "85  4.559330e+07   3.63  0.45375  \n",
       "86  6.370030e+07   6.25  0.78125  \n",
       "87  3.140000e+08   5.88  0.73500  \n",
       "88  3.396753e+06   5.47  0.68375  \n",
       "89  2.977450e+07   3.44  0.43000  \n",
       "90  2.985424e+07   2.87  0.35875  \n",
       "91  8.877290e+07   3.87  0.48375  \n",
       "92  1.478658e+07   3.66  0.45750  \n",
       "\n",
       "[93 rows x 16 columns]"
      ]
     },
     "execution_count": 780,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove a symbol from your dataset using a loop\n",
    "symbols = ['-', '&', '$', '%']\n",
    "for i in symbols:\n",
    "    c['Region'] = c['Region'].str.replace(i,'')\n",
    "\n",
    "# We can code this loop in one line!\n",
    "for i in symbols: c['Region'] = c['Region'].str.replace(i,'')\n",
    "\n",
    "#or replace a particular value\n",
    "c['Region'] = c['Region'].str.replace('Eastern Europe Central Asia', 'CEE')\n",
    "# anything in a quoation is a string - So, I am telling the DataFrame to replace \"Eastern Europe Central Asia with \"CEE\"\n",
    "#in the column Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Income Group</th>\n",
       "      <th>High income</th>\n",
       "      <th>Low income</th>\n",
       "      <th>Lower middle income</th>\n",
       "      <th>Upper middle income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4543</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>0.3475</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>0.3039</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4319</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.2753</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4637</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5779</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4637</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5881</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.3357</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cote d'Ivoire</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5866</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>0.3213</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>0.2626</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.2895</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominican Republic</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5185</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>El Salvador</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4166</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>0.3335</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5551</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>0.2702</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4234</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Panama</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5282</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>0.3294</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>0.3634</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>0.3276</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senegal</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5804</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sierra Leone</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5607</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>0.4007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>0.2557</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>0.3578</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4033</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.2731</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5719</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5868</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4411</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.3237</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>0.4065</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4609</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4987</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6356</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Income Group            High income  Low income  Lower middle income  \\\n",
       "Country                                                                \n",
       "Albania                      0.0000      0.0000               0.4543   \n",
       "Argentina                    0.0000      0.0000               0.0000   \n",
       "Australia                    0.3475      0.0000               0.0000   \n",
       "Austria                      0.3039      0.0000               0.0000   \n",
       "Bangladesh                   0.0000      0.4319               0.0000   \n",
       "Belarus                      0.0000      0.0000               0.0000   \n",
       "Belgium                      0.2753      0.0000               0.0000   \n",
       "Bolivia                      0.0000      0.0000               0.4637   \n",
       "Bosnia and Herzegovina       0.0000      0.0000               0.0000   \n",
       "Botswana                     0.0000      0.0000               0.0000   \n",
       "Brazil                       0.0000      0.0000               0.0000   \n",
       "Bulgaria                     0.0000      0.0000               0.0000   \n",
       "Burkina Faso                 0.0000      0.5779               0.0000   \n",
       "Cambodia                     0.0000      0.4637               0.0000   \n",
       "Cameroon                     0.0000      0.0000               0.5881   \n",
       "Canada                       0.3357      0.0000               0.0000   \n",
       "Chile                        0.0000      0.0000               0.0000   \n",
       "China                        0.0000      0.0000               0.0000   \n",
       "Colombia                     0.0000      0.0000               0.0000   \n",
       "Cote d'Ivoire                0.0000      0.0000               0.5866   \n",
       "Croatia                      0.3213      0.0000               0.0000   \n",
       "Czech Republic               0.2626      0.0000               0.0000   \n",
       "Denmark                      0.2895      0.0000               0.0000   \n",
       "Dominican Republic           0.0000      0.0000               0.0000   \n",
       "Ecuador                      0.0000      0.0000               0.0000   \n",
       "Egypt                        0.0000      0.0000               0.5185   \n",
       "El Salvador                  0.0000      0.0000               0.4166   \n",
       "Estonia                      0.3335      0.0000               0.0000   \n",
       "Ethiopia                     0.0000      0.5551               0.0000   \n",
       "Finland                      0.2702      0.0000               0.0000   \n",
       "...                             ...         ...                  ...   \n",
       "Pakistan                     0.0000      0.0000               0.4234   \n",
       "Panama                       0.0000      0.0000               0.0000   \n",
       "Peru                         0.0000      0.0000               0.0000   \n",
       "Philippines                  0.0000      0.0000               0.5282   \n",
       "Poland                       0.3294      0.0000               0.0000   \n",
       "Portugal                     0.3634      0.0000               0.0000   \n",
       "Republic of Korea            0.3276      0.0000               0.0000   \n",
       "Russia                       0.0000      0.0000               0.0000   \n",
       "Senegal                      0.0000      0.0000               0.5804   \n",
       "Serbia                       0.0000      0.0000               0.0000   \n",
       "Sierra Leone                 0.0000      0.5607               0.0000   \n",
       "Singapore                    0.4007      0.0000               0.0000   \n",
       "Slovenia                     0.2557      0.0000               0.0000   \n",
       "South Africa                 0.0000      0.0000               0.0000   \n",
       "Spain                        0.3578      0.0000               0.0000   \n",
       "Sri Lanka                    0.0000      0.0000               0.4033   \n",
       "Sweden                       0.2731      0.0000               0.0000   \n",
       "Tanzania                     0.0000      0.5719               0.0000   \n",
       "Thailand                     0.0000      0.0000               0.0000   \n",
       "Tunisia                      0.0000      0.0000               0.0000   \n",
       "Turkey                       0.0000      0.0000               0.0000   \n",
       "Uganda                       0.0000      0.5868               0.0000   \n",
       "Ukraine                      0.0000      0.0000               0.4411   \n",
       "United Kingdom               0.3237      0.0000               0.0000   \n",
       "United States                0.4065      0.0000               0.0000   \n",
       "Uruguay                      0.0000      0.0000               0.0000   \n",
       "Uzbekistan                   0.0000      0.0000               0.4609   \n",
       "Venezuela                    0.0000      0.0000               0.0000   \n",
       "Vietnam                      0.0000      0.0000               0.4987   \n",
       "Zambia                       0.0000      0.0000               0.6356   \n",
       "\n",
       "Income Group            Upper middle income  \n",
       "Country                                      \n",
       "Albania                              0.0000  \n",
       "Argentina                            0.4198  \n",
       "Australia                            0.0000  \n",
       "Austria                              0.0000  \n",
       "Bangladesh                           0.0000  \n",
       "Belarus                              0.4315  \n",
       "Belgium                              0.0000  \n",
       "Bolivia                              0.0000  \n",
       "Bosnia and Herzegovina               0.4351  \n",
       "Botswana                             0.6324  \n",
       "Brazil                               0.4596  \n",
       "Bulgaria                             0.3615  \n",
       "Burkina Faso                         0.0000  \n",
       "Cambodia                             0.0000  \n",
       "Cameroon                             0.0000  \n",
       "Canada                               0.0000  \n",
       "Chile                                0.4914  \n",
       "China                                0.5234  \n",
       "Colombia                             0.5184  \n",
       "Cote d'Ivoire                        0.0000  \n",
       "Croatia                              0.0000  \n",
       "Czech Republic                       0.0000  \n",
       "Denmark                              0.0000  \n",
       "Dominican Republic                   0.4588  \n",
       "Ecuador                              0.4648  \n",
       "Egypt                                0.0000  \n",
       "El Salvador                          0.0000  \n",
       "Estonia                              0.0000  \n",
       "Ethiopia                             0.0000  \n",
       "Finland                              0.0000  \n",
       "...                                     ...  \n",
       "Pakistan                             0.0000  \n",
       "Panama                               0.5191  \n",
       "Peru                                 0.4516  \n",
       "Philippines                          0.0000  \n",
       "Poland                               0.0000  \n",
       "Portugal                             0.0000  \n",
       "Republic of Korea                    0.0000  \n",
       "Russia                               0.4812  \n",
       "Senegal                              0.0000  \n",
       "Serbia                               0.3556  \n",
       "Sierra Leone                         0.0000  \n",
       "Singapore                            0.0000  \n",
       "Slovenia                             0.0000  \n",
       "South Africa                         0.6624  \n",
       "Spain                                0.0000  \n",
       "Sri Lanka                            0.0000  \n",
       "Sweden                               0.0000  \n",
       "Tanzania                             0.0000  \n",
       "Thailand                             0.5157  \n",
       "Tunisia                              0.5643  \n",
       "Turkey                               0.4582  \n",
       "Uganda                               0.0000  \n",
       "Ukraine                              0.0000  \n",
       "United Kingdom                       0.0000  \n",
       "United States                        0.0000  \n",
       "Uruguay                              0.4146  \n",
       "Uzbekistan                           0.0000  \n",
       "Venezuela                            0.4334  \n",
       "Vietnam                              0.0000  \n",
       "Zambia                               0.0000  \n",
       "\n",
       "[93 rows x 4 columns]"
      ]
     },
     "execution_count": 781,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape the data\n",
    "f = c.groupby('Income Group').mean().reset_index()\n",
    "c.pivot(index='Country', columns='Income Group', values = 'gini').fillna(0).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#if you forget the details of a function, you can always call it with a ?\n",
    "c.pivot?\n",
    "#the documentation should pop up on the bottom of your browser\n",
    "# you an always x or esc out of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>govt powers</th>\n",
       "      <th>corruption</th>\n",
       "      <th>conflict &amp; violence</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>laws</th>\n",
       "      <th>regulatory</th>\n",
       "      <th>criminal justice</th>\n",
       "      <th>criminal system</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>total</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>9.300000e+01</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.575054</td>\n",
       "      <td>0.536129</td>\n",
       "      <td>0.711505</td>\n",
       "      <td>0.621720</td>\n",
       "      <td>0.527419</td>\n",
       "      <td>0.545161</td>\n",
       "      <td>0.559140</td>\n",
       "      <td>0.530968</td>\n",
       "      <td>0.449614</td>\n",
       "      <td>6.776855e+07</td>\n",
       "      <td>4.607097</td>\n",
       "      <td>0.575887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.168540</td>\n",
       "      <td>0.209352</td>\n",
       "      <td>0.131496</td>\n",
       "      <td>0.147247</td>\n",
       "      <td>0.162554</td>\n",
       "      <td>0.153571</td>\n",
       "      <td>0.123497</td>\n",
       "      <td>0.159449</td>\n",
       "      <td>0.103406</td>\n",
       "      <td>1.928195e+08</td>\n",
       "      <td>1.143398</td>\n",
       "      <td>0.142925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.255701</td>\n",
       "      <td>1.322696e+06</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>0.346250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.361473</td>\n",
       "      <td>6.318000e+06</td>\n",
       "      <td>3.720000</td>\n",
       "      <td>0.465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.458192</td>\n",
       "      <td>1.679142e+07</td>\n",
       "      <td>4.190000</td>\n",
       "      <td>0.523750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.519987</td>\n",
       "      <td>5.000444e+07</td>\n",
       "      <td>5.470000</td>\n",
       "      <td>0.683750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.662355</td>\n",
       "      <td>1.350000e+09</td>\n",
       "      <td>7.120000</td>\n",
       "      <td>0.890000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       govt powers  corruption  conflict & violence    Freedom       laws  \\\n",
       "count    93.000000   93.000000            93.000000  93.000000  93.000000   \n",
       "mean      0.575054    0.536129             0.711505   0.621720   0.527419   \n",
       "std       0.168540    0.209352             0.131496   0.147247   0.162554   \n",
       "min       0.240000    0.200000             0.290000   0.270000   0.260000   \n",
       "25%       0.450000    0.380000             0.620000   0.500000   0.410000   \n",
       "50%       0.550000    0.490000             0.730000   0.610000   0.480000   \n",
       "75%       0.710000    0.740000             0.820000   0.730000   0.620000   \n",
       "max       0.930000    0.960000             0.930000   0.930000   0.930000   \n",
       "\n",
       "       regulatory  criminal justice  criminal system       gini    population  \\\n",
       "count   93.000000         93.000000        93.000000  93.000000  9.300000e+01   \n",
       "mean     0.545161          0.559140         0.530968   0.449614  6.776855e+07   \n",
       "std      0.153571          0.123497         0.159449   0.103406  1.928195e+08   \n",
       "min      0.230000          0.320000         0.240000   0.255701  1.322696e+06   \n",
       "25%      0.430000          0.470000         0.410000   0.361473  6.318000e+06   \n",
       "50%      0.520000          0.540000         0.490000   0.458192  1.679142e+07   \n",
       "75%      0.630000          0.650000         0.660000   0.519987  5.000444e+07   \n",
       "max      0.890000          0.820000         0.870000   0.662355  1.350000e+09   \n",
       "\n",
       "           total        avg  \n",
       "count  93.000000  93.000000  \n",
       "mean    4.607097   0.575887  \n",
       "std     1.143398   0.142925  \n",
       "min     2.770000   0.346250  \n",
       "25%     3.720000   0.465000  \n",
       "50%     4.190000   0.523750  \n",
       "75%     5.470000   0.683750  \n",
       "max     7.120000   0.890000  "
      ]
     },
     "execution_count": 785,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#some basic statistics\n",
    "desc = c.describe()\n",
    "desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrrrrrr}\n",
      "\\toprule\n",
      "{} &  govt powers &  corruption &  conflict \\& violence &    Freedom &       laws &  regulatory &  criminal justice &  criminal system &       gini &    population &      total &        avg \\\\\n",
      "\\midrule\n",
      "count &    93.000000 &   93.000000 &            93.000000 &  93.000000 &  93.000000 &   93.000000 &         93.000000 &        93.000000 &  93.000000 &  9.300000e+01 &  93.000000 &  93.000000 \\\\\n",
      "mean  &     0.575054 &    0.536129 &             0.711505 &   0.621720 &   0.527419 &    0.545161 &          0.559140 &         0.530968 &   0.449614 &  6.776855e+07 &   4.607097 &   0.575887 \\\\\n",
      "std   &     0.168540 &    0.209352 &             0.131496 &   0.147247 &   0.162554 &    0.153571 &          0.123497 &         0.159449 &   0.103406 &  1.928195e+08 &   1.143398 &   0.142925 \\\\\n",
      "min   &     0.240000 &    0.200000 &             0.290000 &   0.270000 &   0.260000 &    0.230000 &          0.320000 &         0.240000 &   0.255701 &  1.322696e+06 &   2.770000 &   0.346250 \\\\\n",
      "25\\%   &     0.450000 &    0.380000 &             0.620000 &   0.500000 &   0.410000 &    0.430000 &          0.470000 &         0.410000 &   0.361473 &  6.318000e+06 &   3.720000 &   0.465000 \\\\\n",
      "50\\%   &     0.550000 &    0.490000 &             0.730000 &   0.610000 &   0.480000 &    0.520000 &          0.540000 &         0.490000 &   0.458192 &  1.679142e+07 &   4.190000 &   0.523750 \\\\\n",
      "75\\%   &     0.710000 &    0.740000 &             0.820000 &   0.730000 &   0.620000 &    0.630000 &          0.650000 &         0.660000 &   0.519987 &  5.000444e+07 &   5.470000 &   0.683750 \\\\\n",
      "max   &     0.930000 &    0.960000 &             0.930000 &   0.930000 &   0.930000 &    0.890000 &          0.820000 &         0.870000 &   0.662355 &  1.350000e+09 &   7.120000 &   0.890000 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#export this table in latex\n",
    "print(desc.to_latex())\n",
    "#you can copy and paste this directly to LaTex\n",
    "\n",
    "#or save it in a latex document\n",
    "desc.to_latex('latexineq.tex')\n",
    "#sometimes you may want to use the option index=False if your index is not needed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphing\n",
    "Seaborn is more intuitive, but to make graphs more detailed it does take more time:\n",
    "\n",
    "Lot's of examplesin seaborn's [gallery](http://seaborn.pydata.org/examples/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mkaltenberg/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
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EGJWBbRgG4T27aV/3KsG6WgBzZbIFVeaCJ/IJVQiRgVSHA8e4ceTkTMFWHsUwDPRQiHhL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mAAAD\n/0lEQVSPf/xjnn76aQzDoLq6mjvvvLNgY21tbcXn81m+HjyUWNeuXctXv/pV7HY7kyZN4qGHHiro\neAEeeughnE4nX//619MnHApB7zi///3v841vfAPDMFizZg3jxo2zOryiJN26hBBCiCIwKqbEhRBC\niGInCVsIIYQoApKwhRBCiCIgCVsIIYQoApKwhRBCiCIgCVuIAvdP//RP7Ny5E4C/+qu/Yvfu3RZH\nJISwgiRsIQrcjh07SKVSAPz4xz9m4cKFFkckhLCCJGwhhsAwDF588UXuu+8+Vq5cySuvvMLx48d5\n/PHHWb16NWvXrmXXrl0AfP/73+epp57i/vvvZ8uWLdx99918+9vf5r777mPXrl3cfffd6eu+9NJL\n6YYJt9xyCz/4wQ9YvXo1jz76KKdOneLNN99kz549/PVf/zUHDx7k8ccf55NPPgHgX//1X1m5ciWr\nV6/mhRdeIJVKcerUKb74xS/yF3/xFzzwwAM8+eSTtLe35/8XJoTIOknYQgzBu+++S319PRs2bODX\nv/4169ev56mnnuLxxx9nw4YN/OAHP+DP//zPicfjgFnve+PGjenkfMcdd7Bp06YBK1G1tbVx0003\nsWHDBlatWsVzzz3HF7/4RRYsWMBzzz3H7Nmz01/7+9//ni1btrB+/Xr+53/+h+bmZl5//XXArIn9\n9a9/nbfeeotAIMCGDRty+JsRQuSLJGwhhmDHjh3cf//9OBwOvF4vr732Gm1tbaxYsQIwWx+WlJRw\n7NgxABYtWtTn+xcvXjzoPZxOJ1/84hcBs9xkz0i6Px9//DGrVq3C5XKhaRpr1qxJd+waM2YM8+bN\nA8zuUx0dHdf+AwshCo4kbCGG4PLuUydPnuTyqr6GYaTXml0uV5/P9fRYVxSlz/f1bu2oqmq63rau\n6wP2jdZ1/YrXeq7Vu5/75fcTQhQvSdhCDMGNN97I7373OxKJBJFIhG9/+9soisJ7770HmJ2ULly4\nMGjXrEAgQEdHB62trcTjcf7whz+kPxeJRNiyZQsA69ev54477gDMHsg9DwI9brnlFt5++22i0SjJ\nZJL//u//5pZbbsnmjyyEKDCjoluXEMO1fPly9uzZw8MPP4yu6zzxxBPcfPPNrFu3jpdeegm73c5L\nL72Ew+EY8Dp+v59vfOMbPPLII1x33XVX7Ph+9913+fu//3vGjh3LT37yEwCWLl3K//k//yf9McBd\nd93F/v37WbNmDclkkqVLl/LYY49x9uzZ7P/wQoiCIN26hCgQs2fP5uDBg1aHIYQoUDIlLoQQQhQB\nGWELIYQQRUBG2EIIIUQRkIQthBBCFAFJ2EIIIUQRkIQthBBCFAFJ2EIIIUQRkIQthBBCFIH/H8VQ\nUwQ3/HWoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x142abf748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's graph a simple regression\n",
    "sns.set(style=\"darkgrid\", color_codes=True)\n",
    "\n",
    "g = sns.jointplot(\"corruption\", \"gini\", data=c, kind=\"reg\",\n",
    "                  xlim=(0.15, 1.0), ylim=(0.15, 0.8), color=\"r\", size=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tuc0vv/yS2NhYnJycmDFjhjkc/lx5Y7d02otWRETEgmzeeZj4Tbs5dioPn7oehPZ4tFKr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e8x+P6ZmdOulWXk1KwlZ8NaMlYuIzDopWvfIU4899KMqVcRcMkCLknEKQkImIE0Q5YJe5IZ9I0z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\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gSTPaQcKFdsBaPybOJLom8Ez1ufAsfaK4whAwZ6v0jznboGwcHkJXoBulrPRCPn25pTwY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\nRCyMAp6IiIiIhfl/GsuwWBHoLfUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147ad2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Multi Regression\n",
    "sns.set(style=\"darkgrid\", color_codes=True)\n",
    "c=c.sort_values('gini')\n",
    "#I sorted this by inequality level so that my graph will have the country lines sorted by lowest to highest inequality\n",
    "g = sns.lmplot(\"corruption\", \"gini\", data=c, hue='Country', palette= \"RdBu_r\", size=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UTk6Oli5dqvLycv3pT39SYWGh1qxZo8WLF8vhcGjChAlav369JKlRo0Z67bXX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x142ca8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Graph\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "ig = c.groupby('Income Group').mean().reset_index()\n",
    "\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = sns.barplot(x=\"Income Group\", y=\"laws\", data=ig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bc83e10>"
      ]
     },
     "execution_count": 792,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mTZo8l6fauHEjXl5e9O/fn44dO3L27Fm2bNlCnz59KCgoeG3FdUEQhMomGqtK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134e53e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc=c[['Country','govt powers',\n",
    "       'corruption', 'conflict & violence', 'Freedom', 'laws', 'regulatory',\n",
    "       'criminal justice', 'criminal system']].transpose()\n",
    "cc.columns=cc.iloc[0]\n",
    "cc= cc.drop('Country',axis=0).fillna(0)\n",
    "cc=cc.transpose()\n",
    "\n",
    "f, ax = plt.subplots(figsize=(5, 7))\n",
    "sns.heatmap(cc,ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#I'm defining a function that I can recall later\n",
    "# the blue is the function name followed by arguments (not all have to be required arguments)\n",
    "def nrm(data, column):\n",
    "    m = np.mean(column)\n",
    "    s = np.std(column)\n",
    "    data['n'] = (column - m)/ s\n",
    "\n",
    "    '''\n",
    "    nrm normalizes a column and adds that normalized column to your DataFrame\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data: dataframe\n",
    "    column: column in your dataframe\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>isocode</th>\n",
       "      <th>govt powers</th>\n",
       "      <th>corruption</th>\n",
       "      <th>conflict &amp; violence</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>laws</th>\n",
       "      <th>regulatory</th>\n",
       "      <th>criminal justice</th>\n",
       "      <th>criminal system</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>total</th>\n",
       "      <th>avg</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>SVN</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.255701</td>\n",
       "      <td>2.057159e+06</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0.65625</td>\n",
       "      <td>-1.885409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>NOR</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.257714</td>\n",
       "      <td>5.018573e+06</td>\n",
       "      <td>6.95</td>\n",
       "      <td>0.86875</td>\n",
       "      <td>-1.865841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>CZE</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.262622</td>\n",
       "      <td>1.051078e+07</td>\n",
       "      <td>5.36</td>\n",
       "      <td>0.67000</td>\n",
       "      <td>-1.818123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.270207</td>\n",
       "      <td>5.413971e+06</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.87000</td>\n",
       "      <td>-1.744371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>SWE</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.273059</td>\n",
       "      <td>9.519374e+06</td>\n",
       "      <td>7.12</td>\n",
       "      <td>0.89000</td>\n",
       "      <td>-1.716637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>BEL</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.275339</td>\n",
       "      <td>1.112825e+07</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.74750</td>\n",
       "      <td>-1.694471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>NLD</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.279401</td>\n",
       "      <td>1.675496e+07</td>\n",
       "      <td>6.82</td>\n",
       "      <td>0.85250</td>\n",
       "      <td>-1.654973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>DNK</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>5.591572e+06</td>\n",
       "      <td>7.03</td>\n",
       "      <td>0.87875</td>\n",
       "      <td>-1.557040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>AUT</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.303947</td>\n",
       "      <td>8.429991e+06</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.80375</td>\n",
       "      <td>-1.416318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>DEU</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.305918</td>\n",
       "      <td>8.052400e+07</td>\n",
       "      <td>6.32</td>\n",
       "      <td>0.79000</td>\n",
       "      <td>-1.397148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Japan</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>JPN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.314224</td>\n",
       "      <td>1.280000e+08</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.80625</td>\n",
       "      <td>-1.316395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>HUN</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.316564</td>\n",
       "      <td>9.920362e+06</td>\n",
       "      <td>5.21</td>\n",
       "      <td>0.65125</td>\n",
       "      <td>-1.293645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>HRV</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.321295</td>\n",
       "      <td>4.267558e+06</td>\n",
       "      <td>4.65</td>\n",
       "      <td>0.58125</td>\n",
       "      <td>-1.247641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>GBR</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.323718</td>\n",
       "      <td>6.370030e+07</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.78125</td>\n",
       "      <td>-1.224086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>KOR</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.327560</td>\n",
       "      <td>5.000444e+07</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.73375</td>\n",
       "      <td>-1.186731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>POL</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.329352</td>\n",
       "      <td>3.806316e+07</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.71500</td>\n",
       "      <td>-1.169299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>France</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>FRA</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.330800</td>\n",
       "      <td>6.563998e+07</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0.76375</td>\n",
       "      <td>-1.155223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333455</td>\n",
       "      <td>1.322696e+06</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0.75875</td>\n",
       "      <td>-1.129410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>CAN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.335666</td>\n",
       "      <td>3.475431e+07</td>\n",
       "      <td>6.35</td>\n",
       "      <td>0.79375</td>\n",
       "      <td>-1.107914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>AUS</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.347525</td>\n",
       "      <td>2.272825e+07</td>\n",
       "      <td>6.59</td>\n",
       "      <td>0.82375</td>\n",
       "      <td>-0.992603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.350970</td>\n",
       "      <td>5.953972e+07</td>\n",
       "      <td>5.05</td>\n",
       "      <td>0.63125</td>\n",
       "      <td>-0.959113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>SRB</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.355623</td>\n",
       "      <td>7.199077e+06</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.50625</td>\n",
       "      <td>-0.913871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>ESP</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.357847</td>\n",
       "      <td>4.677306e+07</td>\n",
       "      <td>5.82</td>\n",
       "      <td>0.72750</td>\n",
       "      <td>-0.892247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BGR</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.361473</td>\n",
       "      <td>7.305888e+06</td>\n",
       "      <td>4.38</td>\n",
       "      <td>0.54750</td>\n",
       "      <td>-0.856990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.363438</td>\n",
       "      <td>1.051484e+07</td>\n",
       "      <td>5.31</td>\n",
       "      <td>0.66375</td>\n",
       "      <td>-0.837886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Greece</td>\n",
       "      <td>Western Europe  North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>GRC</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.364873</td>\n",
       "      <td>1.109277e+07</td>\n",
       "      <td>4.81</td>\n",
       "      <td>0.60125</td>\n",
       "      <td>-0.823929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>NPL</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.380434</td>\n",
       "      <td>2.750052e+07</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0.49750</td>\n",
       "      <td>-0.672635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>SGP</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.400666</td>\n",
       "      <td>5.312400e+06</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.80375</td>\n",
       "      <td>-0.475919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Macedonia</td>\n",
       "      <td>Eastern Europe  Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>MKD</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.402952</td>\n",
       "      <td>2.069270e+06</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.58750</td>\n",
       "      <td>-0.453686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>LKA</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.403273</td>\n",
       "      <td>2.032800e+07</td>\n",
       "      <td>4.37</td>\n",
       "      <td>0.54625</td>\n",
       "      <td>-0.450570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>High income</td>\n",
       "      <td>NZL</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.509161</td>\n",
       "      <td>4.408100e+06</td>\n",
       "      <td>6.73</td>\n",
       "      <td>0.84125</td>\n",
       "      <td>0.578979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>MEX</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.510108</td>\n",
       "      <td>1.220000e+08</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>0.588186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>THA</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.515705</td>\n",
       "      <td>6.716413e+07</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.53250</td>\n",
       "      <td>0.642607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>COL</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>4.688102e+07</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.49500</td>\n",
       "      <td>0.669270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>EGY</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.518462</td>\n",
       "      <td>8.566090e+07</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.50125</td>\n",
       "      <td>0.669410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Panama</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>PAN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.519092</td>\n",
       "      <td>3.743761e+06</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.52250</td>\n",
       "      <td>0.675535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>IDN</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.519987</td>\n",
       "      <td>2.480000e+08</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.52375</td>\n",
       "      <td>0.684239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>China</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>CHN</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.523397</td>\n",
       "      <td>1.350000e+09</td>\n",
       "      <td>3.81</td>\n",
       "      <td>0.47625</td>\n",
       "      <td>0.717393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>East Asia  Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>PHL</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>9.601732e+07</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.49500</td>\n",
       "      <td>0.764292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>ETH</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.555068</td>\n",
       "      <td>9.219121e+07</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.42125</td>\n",
       "      <td>1.025333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>JOR</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.559149</td>\n",
       "      <td>6.318000e+06</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.57375</td>\n",
       "      <td>1.065015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>GHA</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.560337</td>\n",
       "      <td>2.554456e+07</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.58750</td>\n",
       "      <td>1.076560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>SLE</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.560696</td>\n",
       "      <td>6.043157e+06</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.45875</td>\n",
       "      <td>1.080056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>TUN</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.564304</td>\n",
       "      <td>1.077750e+07</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.56750</td>\n",
       "      <td>1.115134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>LBR</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.564524</td>\n",
       "      <td>4.190155e+06</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.40875</td>\n",
       "      <td>1.117275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>TZA</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.571853</td>\n",
       "      <td>4.864571e+07</td>\n",
       "      <td>3.92</td>\n",
       "      <td>0.49000</td>\n",
       "      <td>1.188531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Iran</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>IRN</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.573965</td>\n",
       "      <td>7.615698e+07</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.47500</td>\n",
       "      <td>1.209072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>BFA</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.577919</td>\n",
       "      <td>1.659081e+07</td>\n",
       "      <td>4.23</td>\n",
       "      <td>0.52875</td>\n",
       "      <td>1.247520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>MDG</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.578732</td>\n",
       "      <td>2.229372e+07</td>\n",
       "      <td>4.16</td>\n",
       "      <td>0.52000</td>\n",
       "      <td>1.255419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>SEN</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.580372</td>\n",
       "      <td>1.378011e+07</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.54500</td>\n",
       "      <td>1.271367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>Middle East  North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>MAR</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.581034</td>\n",
       "      <td>3.298419e+07</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.49625</td>\n",
       "      <td>1.277798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>CIV</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.586571</td>\n",
       "      <td>2.110264e+07</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.45375</td>\n",
       "      <td>1.331640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>UGA</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.586754</td>\n",
       "      <td>3.540062e+07</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.41750</td>\n",
       "      <td>1.333413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>CMR</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.588098</td>\n",
       "      <td>2.165949e+07</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.34625</td>\n",
       "      <td>1.346486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>MWI</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.594464</td>\n",
       "      <td>1.570044e+07</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.50125</td>\n",
       "      <td>1.408377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>Latin America  Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>JAM</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.594827</td>\n",
       "      <td>2.707805e+06</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.52375</td>\n",
       "      <td>1.411915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>KEN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.605056</td>\n",
       "      <td>4.254298e+07</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.44750</td>\n",
       "      <td>1.511369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>BWA</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.632413</td>\n",
       "      <td>2.132822e+06</td>\n",
       "      <td>5.58</td>\n",
       "      <td>0.69750</td>\n",
       "      <td>1.777360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.635562</td>\n",
       "      <td>1.478658e+07</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.45750</td>\n",
       "      <td>1.807976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>SubSaharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.662355</td>\n",
       "      <td>5.234170e+07</td>\n",
       "      <td>4.51</td>\n",
       "      <td>0.56375</td>\n",
       "      <td>2.068485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country                         Region         Income Group  \\\n",
       "75           Slovenia   Eastern Europe  Central Asia          High income   \n",
       "62             Norway  Western Europe  North America          High income   \n",
       "21     Czech Republic   Eastern Europe  Central Asia          High income   \n",
       "29            Finland  Western Europe  North America          High income   \n",
       "79             Sweden  Western Europe  North America          High income   \n",
       "6             Belgium  Western Europe  North America          High income   \n",
       "58        Netherlands  Western Europe  North America          High income   \n",
       "22            Denmark  Western Europe  North America          High income   \n",
       "3             Austria  Western Europe  North America          High income   \n",
       "32            Germany  Western Europe  North America          High income   \n",
       "43              Japan             East Asia  Pacific          High income   \n",
       "37            Hungary   Eastern Europe  Central Asia          High income   \n",
       "20            Croatia   Eastern Europe  Central Asia          High income   \n",
       "86     United Kingdom  Western Europe  North America          High income   \n",
       "69  Republic of Korea             East Asia  Pacific          High income   \n",
       "67             Poland   Eastern Europe  Central Asia          High income   \n",
       "30             France  Western Europe  North America          High income   \n",
       "27            Estonia   Eastern Europe  Central Asia          High income   \n",
       "15             Canada  Western Europe  North America          High income   \n",
       "2           Australia             East Asia  Pacific          High income   \n",
       "41              Italy  Western Europe  North America          High income   \n",
       "72             Serbia   Eastern Europe  Central Asia  Upper middle income   \n",
       "77              Spain  Western Europe  North America          High income   \n",
       "11           Bulgaria   Eastern Europe  Central Asia  Upper middle income   \n",
       "68           Portugal  Western Europe  North America          High income   \n",
       "34             Greece  Western Europe  North America          High income   \n",
       "57              Nepal                     South Asia           Low income   \n",
       "74          Singapore             East Asia  Pacific          High income   \n",
       "49          Macedonia   Eastern Europe  Central Asia  Upper middle income   \n",
       "78          Sri Lanka                     South Asia  Lower middle income   \n",
       "..                ...                            ...                  ...   \n",
       "59        New Zealand             East Asia  Pacific          High income   \n",
       "53             Mexico       Latin America  Caribbean  Upper middle income   \n",
       "81           Thailand             East Asia  Pacific  Upper middle income   \n",
       "18           Colombia       Latin America  Caribbean  Upper middle income   \n",
       "25              Egypt      Middle East  North Africa  Lower middle income   \n",
       "64             Panama       Latin America  Caribbean  Upper middle income   \n",
       "39          Indonesia             East Asia  Pacific  Lower middle income   \n",
       "17              China             East Asia  Pacific  Upper middle income   \n",
       "66        Philippines             East Asia  Pacific  Lower middle income   \n",
       "28           Ethiopia              SubSaharan Africa           Low income   \n",
       "44             Jordan      Middle East  North Africa  Upper middle income   \n",
       "33              Ghana              SubSaharan Africa           Low income   \n",
       "73       Sierra Leone              SubSaharan Africa           Low income   \n",
       "82            Tunisia      Middle East  North Africa  Upper middle income   \n",
       "48            Liberia              SubSaharan Africa           Low income   \n",
       "80           Tanzania              SubSaharan Africa           Low income   \n",
       "40               Iran      Middle East  North Africa  Upper middle income   \n",
       "12       Burkina Faso              SubSaharan Africa           Low income   \n",
       "50         Madagascar              SubSaharan Africa           Low income   \n",
       "71            Senegal              SubSaharan Africa  Lower middle income   \n",
       "56            Morocco      Middle East  North Africa  Lower middle income   \n",
       "19      Cote d'Ivoire              SubSaharan Africa  Lower middle income   \n",
       "84             Uganda              SubSaharan Africa           Low income   \n",
       "14           Cameroon              SubSaharan Africa  Lower middle income   \n",
       "51             Malawi              SubSaharan Africa           Low income   \n",
       "42            Jamaica       Latin America  Caribbean  Upper middle income   \n",
       "46              Kenya              SubSaharan Africa           Low income   \n",
       "9            Botswana              SubSaharan Africa  Upper middle income   \n",
       "92             Zambia              SubSaharan Africa  Lower middle income   \n",
       "76       South Africa              SubSaharan Africa  Upper middle income   \n",
       "\n",
       "   isocode  govt powers  corruption  conflict & violence  Freedom  laws  \\\n",
       "75     SVN         0.64        0.62                 0.80     0.78  0.63   \n",
       "62     NOR         0.90        0.94                 0.87     0.90  0.84   \n",
       "21     CZE         0.71        0.62                 0.81     0.79  0.49   \n",
       "29     FIN         0.89        0.93                 0.92     0.90  0.84   \n",
       "79     SWE         0.92        0.96                 0.89     0.93  0.93   \n",
       "6      BEL         0.78        0.78                 0.84     0.81  0.67   \n",
       "58     NLD         0.86        0.93                 0.86     0.84  0.90   \n",
       "22     DNK         0.93        0.95                 0.91     0.91  0.82   \n",
       "3      AUT         0.82        0.77                 0.89     0.82  0.80   \n",
       "32     DEU         0.82        0.82                 0.86     0.80  0.73   \n",
       "43     JPN         0.80        0.84                 0.89     0.78  0.82   \n",
       "37     HUN         0.63        0.72                 0.83     0.72  0.52   \n",
       "20     HRV         0.61        0.55                 0.77     0.67  0.53   \n",
       "86     GBR         0.79        0.80                 0.84     0.78  0.78   \n",
       "69     KOR         0.66        0.74                 0.82     0.76  0.74   \n",
       "67     POL         0.78        0.72                 0.81     0.85  0.59   \n",
       "30     FRA         0.80        0.80                 0.84     0.79  0.75   \n",
       "27     EST         0.79        0.77                 0.82     0.79  0.71   \n",
       "15     CAN         0.78        0.81                 0.88     0.78  0.84   \n",
       "2      AUS         0.88        0.90                 0.86     0.84  0.84   \n",
       "41     ITA         0.67        0.62                 0.76     0.72  0.49   \n",
       "72     SRB         0.48        0.42                 0.75     0.61  0.44   \n",
       "77     ESP         0.75        0.80                 0.79     0.86  0.61   \n",
       "11     BGR         0.51        0.46                 0.74     0.68  0.53   \n",
       "68     PRT         0.71        0.68                 0.74     0.75  0.62   \n",
       "34     GRC         0.64        0.56                 0.73     0.72  0.51   \n",
       "57     NPL         0.51        0.40                 0.69     0.59  0.38   \n",
       "74     SGP         0.73        0.91                 0.93     0.73  0.67   \n",
       "49     MKD         0.52        0.55                 0.75     0.64  0.62   \n",
       "78     LKA         0.56        0.51                 0.54     0.60  0.50   \n",
       "..     ...          ...         ...                  ...      ...   ...   \n",
       "59     NZL         0.87        0.92                 0.87     0.86  0.84   \n",
       "53     MEX         0.55        0.37                 0.50     0.56  0.53   \n",
       "81     THA         0.53        0.41                 0.63     0.66  0.50   \n",
       "18     COL         0.55        0.44                 0.43     0.55  0.51   \n",
       "25     EGY         0.58        0.51                 0.67     0.43  0.48   \n",
       "64     PAN         0.45        0.41                 0.68     0.63  0.60   \n",
       "39     IDN         0.64        0.30                 0.72     0.56  0.53   \n",
       "17     CHN         0.36        0.52                 0.78     0.35  0.42   \n",
       "66     PHL         0.56        0.41                 0.60     0.57  0.46   \n",
       "28     ETH         0.36        0.44                 0.56     0.41  0.29   \n",
       "44     JOR         0.55        0.57                 0.75     0.50  0.46   \n",
       "33     GHA         0.72        0.45                 0.68     0.72  0.55   \n",
       "73     SLE         0.55        0.36                 0.64     0.63  0.26   \n",
       "82     TUN         0.58        0.52                 0.79     0.56  0.46   \n",
       "48     LBR         0.53        0.36                 0.56     0.52  0.39   \n",
       "80     TZA         0.55        0.41                 0.61     0.53  0.41   \n",
       "40     IRN         0.37        0.49                 0.68     0.27  0.38   \n",
       "12     BFA         0.43        0.50                 0.70     0.59  0.41   \n",
       "50     MDG         0.45        0.39                 0.76     0.58  0.50   \n",
       "71     SEN         0.57        0.49                 0.65     0.62  0.41   \n",
       "56     MAR         0.57        0.33                 0.72     0.48  0.51   \n",
       "19     CIV         0.43        0.39                 0.58     0.50  0.37   \n",
       "84     UGA         0.43        0.32                 0.48     0.43  0.36   \n",
       "14     CMR         0.31        0.20                 0.62     0.42  0.27   \n",
       "51     MWI         0.49        0.44                 0.69     0.47  0.43   \n",
       "42     JAM         0.60        0.51                 0.60     0.59  0.41   \n",
       "46     KEN         0.45        0.27                 0.62     0.54  0.44   \n",
       "9      BWA         0.73        0.75                 0.76     0.59  0.67   \n",
       "92     ZMB         0.51        0.44                 0.67     0.41  0.39   \n",
       "76     ZAF         0.62        0.50                 0.56     0.64  0.61   \n",
       "\n",
       "    regulatory  criminal justice  criminal system      gini    population  \\\n",
       "75        0.59              0.60             0.59  0.255701  2.057159e+06   \n",
       "62        0.83              0.82             0.85  0.257714  5.018573e+06   \n",
       "21        0.59              0.65             0.70  0.262622  1.051078e+07   \n",
       "29        0.82              0.79             0.87  0.270207  5.413971e+06   \n",
       "79        0.89              0.78             0.82  0.273059  9.519374e+06   \n",
       "6         0.70              0.68             0.72  0.275339  1.112825e+07   \n",
       "58        0.83              0.80             0.80  0.279401  1.675496e+07   \n",
       "22        0.85              0.79             0.87  0.289474  5.591572e+06   \n",
       "3         0.84              0.74             0.75  0.303947  8.429991e+06   \n",
       "32        0.73              0.80             0.76  0.305918  8.052400e+07   \n",
       "43        0.87              0.77             0.68  0.314224  1.280000e+08   \n",
       "37        0.60              0.55             0.64  0.316564  9.920362e+06   \n",
       "20        0.48              0.51             0.53  0.321295  4.267558e+06   \n",
       "86        0.79              0.72             0.75  0.323718  6.370030e+07   \n",
       "69        0.67              0.72             0.76  0.327560  5.000444e+07   \n",
       "67        0.61              0.63             0.73  0.329352  3.806316e+07   \n",
       "30        0.76              0.68             0.69  0.330800  6.563998e+07   \n",
       "27        0.73              0.71             0.75  0.333455  1.322696e+06   \n",
       "15        0.79              0.72             0.75  0.335666  3.475431e+07   \n",
       "2         0.83              0.72             0.72  0.347525  2.272825e+07   \n",
       "41        0.56              0.56             0.67  0.350970  5.953972e+07   \n",
       "72        0.43              0.47             0.45  0.355623  7.199077e+06   \n",
       "77        0.67              0.65             0.69  0.357847  4.677306e+07   \n",
       "11        0.50              0.57             0.39  0.361473  7.305888e+06   \n",
       "68        0.57              0.62             0.62  0.363438  1.051484e+07   \n",
       "34        0.54              0.61             0.50  0.364873  1.109277e+07   \n",
       "57        0.44              0.43             0.54  0.380434  2.750052e+07   \n",
       "74        0.80              0.79             0.87  0.400666  5.312400e+06   \n",
       "49        0.56              0.53             0.53  0.402952  2.069270e+06   \n",
       "78        0.52              0.52             0.62  0.403273  2.032800e+07   \n",
       "..         ...               ...              ...       ...           ...   \n",
       "59        0.82              0.76             0.79  0.509161  4.408100e+06   \n",
       "53        0.49              0.40             0.35  0.510108  1.220000e+08   \n",
       "81        0.51              0.43             0.59  0.515705  6.716413e+07   \n",
       "18        0.52              0.53             0.43  0.518447  4.688102e+07   \n",
       "25        0.42              0.47             0.45  0.518462  8.566090e+07   \n",
       "64        0.52              0.51             0.38  0.519092  3.743761e+06   \n",
       "39        0.50              0.49             0.45  0.519987  2.480000e+08   \n",
       "17        0.41              0.43             0.54  0.523397  1.350000e+09   \n",
       "66        0.51              0.43             0.42  0.528220  9.601732e+07   \n",
       "28        0.36              0.46             0.49  0.555068  9.219121e+07   \n",
       "44        0.59              0.65             0.52  0.559149  6.318000e+06   \n",
       "33        0.52              0.61             0.45  0.560337  2.554456e+07   \n",
       "73        0.33              0.54             0.36  0.560696  6.043157e+06   \n",
       "82        0.55              0.56             0.52  0.564304  1.077750e+07   \n",
       "48        0.23              0.33             0.35  0.564524  4.190155e+06   \n",
       "80        0.44              0.48             0.49  0.571853  4.864571e+07   \n",
       "40        0.54              0.62             0.45  0.573965  7.615698e+07   \n",
       "12        0.56              0.59             0.45  0.577919  1.659081e+07   \n",
       "50        0.46              0.53             0.49  0.578732  2.229372e+07   \n",
       "71        0.58              0.58             0.46  0.580372  1.378011e+07   \n",
       "56        0.47              0.54             0.35  0.581034  3.298419e+07   \n",
       "19        0.48              0.51             0.37  0.586571  2.110264e+07   \n",
       "84        0.38              0.51             0.43  0.586754  3.540062e+07   \n",
       "14        0.28              0.35             0.32  0.588098  2.165949e+07   \n",
       "51        0.45              0.59             0.45  0.594464  1.570044e+07   \n",
       "42        0.55              0.51             0.42  0.594827  2.707805e+06   \n",
       "46        0.39              0.47             0.40  0.605056  4.254298e+07   \n",
       "9         0.71              0.65             0.72  0.632413  2.132822e+06   \n",
       "92        0.41              0.46             0.37  0.635562  1.478658e+07   \n",
       "76        0.54              0.55             0.49  0.662355  5.234170e+07   \n",
       "\n",
       "    total      avg         n  \n",
       "75   5.25  0.65625 -1.885409  \n",
       "62   6.95  0.86875 -1.865841  \n",
       "21   5.36  0.67000 -1.818123  \n",
       "29   6.96  0.87000 -1.744371  \n",
       "79   7.12  0.89000 -1.716637  \n",
       "6    5.98  0.74750 -1.694471  \n",
       "58   6.82  0.85250 -1.654973  \n",
       "22   7.03  0.87875 -1.557040  \n",
       "3    6.43  0.80375 -1.416318  \n",
       "32   6.32  0.79000 -1.397148  \n",
       "43   6.45  0.80625 -1.316395  \n",
       "37   5.21  0.65125 -1.293645  \n",
       "20   4.65  0.58125 -1.247641  \n",
       "86   6.25  0.78125 -1.224086  \n",
       "69   5.87  0.73375 -1.186731  \n",
       "67   5.72  0.71500 -1.169299  \n",
       "30   6.11  0.76375 -1.155223  \n",
       "27   6.07  0.75875 -1.129410  \n",
       "15   6.35  0.79375 -1.107914  \n",
       "2    6.59  0.82375 -0.992603  \n",
       "41   5.05  0.63125 -0.959113  \n",
       "72   4.05  0.50625 -0.913871  \n",
       "77   5.82  0.72750 -0.892247  \n",
       "11   4.38  0.54750 -0.856990  \n",
       "68   5.31  0.66375 -0.837886  \n",
       "34   4.81  0.60125 -0.823929  \n",
       "57   3.98  0.49750 -0.672635  \n",
       "74   6.43  0.80375 -0.475919  \n",
       "49   4.70  0.58750 -0.453686  \n",
       "78   4.37  0.54625 -0.450570  \n",
       "..    ...      ...       ...  \n",
       "59   6.73  0.84125  0.578979  \n",
       "53   3.75  0.46875  0.588186  \n",
       "81   4.26  0.53250  0.642607  \n",
       "18   3.96  0.49500  0.669270  \n",
       "25   4.01  0.50125  0.669410  \n",
       "64   4.18  0.52250  0.675535  \n",
       "39   4.19  0.52375  0.684239  \n",
       "17   3.81  0.47625  0.717393  \n",
       "66   3.96  0.49500  0.764292  \n",
       "28   3.37  0.42125  1.025333  \n",
       "44   4.59  0.57375  1.065015  \n",
       "33   4.70  0.58750  1.076560  \n",
       "73   3.67  0.45875  1.080056  \n",
       "82   4.54  0.56750  1.115134  \n",
       "48   3.27  0.40875  1.117275  \n",
       "80   3.92  0.49000  1.188531  \n",
       "40   3.80  0.47500  1.209072  \n",
       "12   4.23  0.52875  1.247520  \n",
       "50   4.16  0.52000  1.255419  \n",
       "71   4.36  0.54500  1.271367  \n",
       "56   3.97  0.49625  1.277798  \n",
       "19   3.63  0.45375  1.331640  \n",
       "84   3.34  0.41750  1.333413  \n",
       "14   2.77  0.34625  1.346486  \n",
       "51   4.01  0.50125  1.408377  \n",
       "42   4.19  0.52375  1.411915  \n",
       "46   3.58  0.44750  1.511369  \n",
       "9    5.58  0.69750  1.777360  \n",
       "92   3.66  0.45750  1.807976  \n",
       "76   4.51  0.56375  2.068485  \n",
       "\n",
       "[93 rows x 17 columns]"
      ]
     },
     "execution_count": 806,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check out our function in action (call it anytime in this document; you can also call function through text (like sublime))\n",
    "nrm(c, c['gini'])\n",
    "c\n",
    "# new column is at the end, n, which I programmed into the function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enjoy!\n",
    "##### Python is well documented so check out some packages,  browse around to explore more functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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